#importing libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from tqdm import tqdm_notebook
from igraph import Graph
import igraph
import Neuroscience_Project_modules as npm
import bct
import random

1. Connectivity graph

##paths 
open_eyes = "./data/S004R01.edf"
closed_eyes = "./data/S004R02.edf"

freq_alpha = 10 ###10Hz Alpha_Wave_Frequency

1.1. (mandatory)

Estimate functional brain connectivity among 64 channels using one of the MVAR estimators: Partial Directed Coherence (PDC), Direct Transfer Function (DTF). Select one relevant frequency value. Apply a threshold so that the resulting binary connectivity matrices have network density equal to 20%. Create a graphical representation of the binary adjacency matrix.

Open Eyes

PDC_open = npm.Connectivity_Graph('PDC')
PDC_open.import_data(open_eyes)
PDC_open.connectivity(freq=freq_alpha,threshold=0.2,plot=True)
print(PDC_open.binary_adjacency_matrix)
plt.imshow(PDC_open.binary_adjacency_matrix,cmap='Greys',interpolation='nearest')

/Users/miguel/anaconda3/lib/python3.7/site-packages/connectivipy/mvar/fitting.py:267: FutureWarning: arrays to stack must be passed as a "sequence" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.
  req = np.vstack(rr_b[:, :, x].T for x in range(1, pmax+1))

best model order p: 7

[[0. 1. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 0. 0.]
 [0. 1. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]]

<matplotlib.image.AxesImage at 0x1c2383dcf8>

Closed Eyes

PDC_closed = npm.Connectivity_Graph('PDC')
PDC_closed.import_data(closed_eyes)
PDC_closed.connectivity(freq=freq_alpha,threshold=0.2,plot=True)
print(PDC_closed.binary_adjacency_matrix)
plt.imshow(PDC_closed.binary_adjacency_matrix,cmap='Greys',interpolation='nearest')

best model order p: 6

[[0. 1. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 0. 0.]
 [0. 1. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 0. 0.]]

<matplotlib.image.AxesImage at 0x1c23480fd0>

1.2. (class ‘A’)

Perform task 1.1 using both estimators (PDC and DTF).

Open Eyes

DTF_open = npm.Connectivity_Graph('DTF')
DTF_open.import_data(open_eyes)
DTF_open.connectivity(freq=freq_alpha,threshold=0.2,plot=True)
print(DTF_open.binary_adjacency_matrix)
plt.imshow(DTF_open.binary_adjacency_matrix,cmap='Greys',interpolation='nearest')

best model order p: 7

[[0. 1. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 0. 0.]]

<matplotlib.image.AxesImage at 0x1c27c592e8>

Closed Eyes

DTF_closed = npm.Connectivity_Graph('DTF')
DTF_closed.import_data(closed_eyes)
DTF_closed.connectivity(freq=freq_alpha,threshold=0.2,plot=True)
print(DTF_closed.binary_adjacency_matrix)
plt.imshow(DTF_closed.binary_adjacency_matrix,cmap='Greys',interpolation='nearest')

best model order p: 6

[[0. 0. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 1. 0.]]

<matplotlib.image.AxesImage at 0x1c27c7fc88>

#Models Order computed from Akaike criterion

order_open = 7
order_closed = 6

1.3.(class ‘A’)

Perform task 1.1 using thresholds yielding the following density values: 1%, 5%, 10%, 20%, 30%, 50%.

m_open = [] 
m_closed = []
densities = [0.01,0.05,0.1,0.2,0.3,0.5]
for d in tqdm_notebook(densities):
    PDC_open = npm.Connectivity_Graph('PDC')
    PDC_open.import_data(open_eyes)
    PDC_open.connectivity(freq=freq_alpha,threshold=d,order=order_open)
    m_open.append(PDC_open.binary_adjacency_matrix)
    PDC_closed = npm.Connectivity_Graph('PDC')
    PDC_closed.import_data(closed_eyes)
    PDC_closed.connectivity(freq=freq_alpha,threshold=d,order=order_closed)
    m_closed.append(PDC_closed.binary_adjacency_matrix)
    
m = [m_open,m_closed]

cols = ['Case: {}'.format(col) for col in ['open','closed']]
rows = ['Density: {}'.format(row) for row in densities]

fig, axes = plt.subplots(nrows=6, ncols=2, figsize=(20,12))

for ax, col in zip(axes[0], cols):
    ax.set_title(col)

for ax, row in zip(axes[:,0], rows):
    ax.set_ylabel(row, rotation=90, size='large')
    
r = 0
for row in axes:
    c = 0
    for col in row:
        col.imshow(m[c][r],cmap='Greys',interpolation='nearest')
        c += 1
    r += 1    

fig.tight_layout()
fig.subplots_adjust(left=0.05, top=0.95)
plt.show()

1.4.(class ‘D’)

Considering the subset of 19 channels suggested in Figure 1 and Table 2, estimate the connectivity using PDC or DTF and apply a statistical validation method (asymptotic statistics 7 , resampling procedure 8 ,...) to filter out values that are not significantly different from 0 (PDC(i, j) ≠ 0 with p < 5%).

channels = 'Fp1 Fp2 F7 F3 Fz F4 F8 T7 C3 Cz C4 T8 P7 P3 Pz P4 P8 O1 O2'.split(' ')

Open Eyes

PDC_open = npm.Connectivity_Graph('PDC')
PDC_open.import_data(open_eyes,channels=channels)
PDC_open.connectivity(freq=freq_alpha,order=order_open)
PDC_open.significance(signf_threshold=0.05,channels=channels,visual=False,path=open_eyes,Nrep=1000)
#PDC_open.significance_matrix

plt.imshow(PDC_open.significance_matrix,cmap='jet_r')

<matplotlib.image.AxesImage at 0x1c23534320>

## associated binary adjacency matrix after insignificant connections removal

plt.imshow(PDC_open.binary_adjacency_matrix,cmap='Greys',interpolation='nearest')

<matplotlib.image.AxesImage at 0x1c23e29668>

Closed Eyes

PDC_closed = npm.Connectivity_Graph('PDC')
PDC_closed.import_data(closed_eyes,channels=channels)
PDC_closed.connectivity(freq=freq_alpha,order=order_closed)
PDC_closed.significance(signf_threshold=0.05,channels=channels,visual=False,path=open_eyes,Nrep=1000)
#PDC_closed.significance_matrix

plt.imshow(PDC_closed.significance_matrix,cmap='jet_r')

<matplotlib.image.AxesImage at 0x1c28b8e1d0>

## associated binary adjacency matrix after insignificant connections removal

plt.imshow(PDC_closed.binary_adjacency_matrix,cmap='Greys',interpolation='nearest')

<matplotlib.image.AxesImage at 0x1c28ffc668>

1.5.(class ‘C’)

Make a topographical representation of the networks (see example in Figure 2). Cartesian coordinates of planar representation of EEG channels are available in Table 3 (see also the file channel_locations.txt).

(the choice of this task is advised in the case of 19-channel networks and/or density ≤ 5%).

Open Eyes

PDC_open = npm.Connectivity_Graph('PDC')
PDC_open.import_data(open_eyes)
PDC_open.connectivity(freq=freq_alpha,threshold=0.05,order=order_open)
PDC_open.show_graph('PDC_case_open')

Closed Eyes

PDC_closed = npm.Connectivity_Graph('PDC')
PDC_closed.import_data(closed_eyes)
PDC_closed.connectivity(freq=freq_alpha,threshold=0.05,order=order_closed)
PDC_closed.show_graph('PDC_case_closed')

/Users/miguel/anaconda3/lib/python3.7/site-packages/connectivipy/mvar/fitting.py:267: FutureWarning: arrays to stack must be passed as a "sequence" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.
  req = np.vstack(rr_b[:, :, x].T for x in range(1, pmax+1))

1.6. (class ‘B’)

Perform task 1.1 considering a second frequency value belonging to a different EEG rhythm with respect to the first choice.

freq_beta = 20 ###20Hz Beta_Wave_Frequency

Open Eyes

PDC_open = npm.Connectivity_Graph('PDC')
PDC_open.import_data(open_eyes)
PDC_open.connectivity(freq=freq_beta,threshold=0.2,order=order_open)
print(PDC_open.binary_adjacency_matrix)
plt.imshow(PDC_open.binary_adjacency_matrix,cmap='Greys',interpolation='nearest')

[[0. 1. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 0. 0.]
 [0. 1. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]]

<matplotlib.image.AxesImage at 0x1c28e8b0f0>

Closed Eyes

PDC_closed = npm.Connectivity_Graph('PDC')
PDC_closed.import_data(closed_eyes)
PDC_closed.connectivity(freq=freq_beta,threshold=0.2,order=order_closed)
print(PDC_closed.binary_adjacency_matrix)
plt.imshow(PDC_closed.binary_adjacency_matrix,cmap='Greys',interpolation='nearest')

[[0. 0. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 1. 0. 1.]
 [0. 0. 0. ... 1. 0. 0.]]

<matplotlib.image.AxesImage at 0x1c28cd0cc0>

2. Graph theory indices

2.1. (mandatory)

Compute binary global (clustering coefficient, path length) and local (degree, in/out-degree) graph indices. List the highest 10 channels for local indices.

Open Eyes

IDX = npm.Graph_Theory_Indices("PDC")
IDX.import_data(open_eyes)
IDX.connectivity(freq=freq_alpha, threshold=0.2, plot=False,order=order_open)

print('\x1b[1;35m'+'\nGlobal indices'+'\x1b[0m \n')
print("APL (Average Path Length) and GCC (Global Clustering Coefficient)\n")
print('\x1b[1;39m'+"APL (w/o weights): "+'\x1b[0m', IDX.average_path_length(False))
print('\x1b[1;39m'+"GCC (w/o weights): "+'\x1b[0m', IDX.global_clustering_coefficient(False))
print("\n")
print('\x1b[1;35m'+'Local indices'+'\x1b[0m \n')
print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m', IDX.local_indegree())
print('\x1b[1;39m'+"Local outdegree: "+'\x1b[0m', IDX.local_outdegree())
print('\x1b[1;39m'+"Local degree: "+'\x1b[0m', IDX.local_degree())

Global indices 

APL (Average Path Length) and GCC (Global Clustering Coefficient)

APL (w/o weights):  1.5830853174603174
GCC (w/o weights):  0.2703346932759426


Local indices 

Local indegree:  {'Fc5': 11, 'Fc3': 16, 'Fc1': 11, 'Fcz': 19, 'Fc2': 13, 'Fc4': 20, 'Fc6': 23, 'C5': 1, 'C3': 11, 'C1': 13, 'Cz': 3, 'C2': 12, 'C4': 7, 'C6': 5, 'Cp5': 5, 'Cp3': 7, 'Cp1': 25, 'Cpz': 7, 'Cp2': 9, 'Cp4': 3, 'Cp6': 23, 'Fp1': 6, 'Fpz': 4, 'Fp2': 1, 'Af7': 6, 'Af3': 0, 'Afz': 6, 'Af4': 4, 'Af8': 0, 'F7': 3, 'F5': 2, 'F3': 7, 'F1': 8, 'Fz': 21, 'F2': 14, 'F4': 19, 'F6': 1, 'F8': 4, 'Ft7': 15, 'Ft8': 0, 'T7': 11, 'T8': 19, 'T9': 6, 'T10': 26, 'Tp7': 9, 'Tp8': 15, 'P7': 1, 'P5': 29, 'P3': 13, 'P1': 25, 'Pz': 25, 'P2': 4, 'P4': 34, 'P6': 25, 'P8': 10, 'Po7': 11, 'Po3': 37, 'Poz': 16, 'Po4': 36, 'Po8': 11, 'O1': 28, 'Oz': 3, 'O2': 27, 'Iz': 20}
Local outdegree:  {'Fc5': 7, 'Fc3': 11, 'Fc1': 9, 'Fcz': 9, 'Fc2': 10, 'Fc4': 3, 'Fc6': 3, 'C5': 13, 'C3': 12, 'C1': 10, 'Cz': 15, 'C2': 11, 'C4': 10, 'C6': 6, 'Cp5': 6, 'Cp3': 10, 'Cp1': 10, 'Cpz': 12, 'Cp2': 12, 'Cp4': 10, 'Cp6': 3, 'Fp1': 22, 'Fpz': 26, 'Fp2': 25, 'Af7': 32, 'Af3': 35, 'Afz': 11, 'Af4': 22, 'Af8': 41, 'F7': 16, 'F5': 23, 'F3': 25, 'F1': 18, 'Fz': 17, 'F2': 8, 'F4': 9, 'F6': 21, 'F8': 14, 'Ft7': 11, 'Ft8': 56, 'T7': 9, 'T8': 5, 'T9': 11, 'T10': 2, 'Tp7': 10, 'Tp8': 8, 'P7': 11, 'P5': 7, 'P3': 10, 'P1': 11, 'Pz': 14, 'P2': 12, 'P4': 11, 'P6': 6, 'P8': 6, 'Po7': 8, 'Po3': 7, 'Poz': 8, 'Po4': 4, 'Po8': 3, 'O1': 6, 'Oz': 5, 'O2': 4, 'Iz': 4}
Local degree:  {'Fc5': 18, 'Fc3': 27, 'Fc1': 20, 'Fcz': 28, 'Fc2': 23, 'Fc4': 23, 'Fc6': 26, 'C5': 14, 'C3': 23, 'C1': 23, 'Cz': 18, 'C2': 23, 'C4': 17, 'C6': 11, 'Cp5': 11, 'Cp3': 17, 'Cp1': 35, 'Cpz': 19, 'Cp2': 21, 'Cp4': 13, 'Cp6': 26, 'Fp1': 28, 'Fpz': 30, 'Fp2': 26, 'Af7': 38, 'Af3': 35, 'Afz': 17, 'Af4': 26, 'Af8': 41, 'F7': 19, 'F5': 25, 'F3': 32, 'F1': 26, 'Fz': 38, 'F2': 22, 'F4': 28, 'F6': 22, 'F8': 18, 'Ft7': 26, 'Ft8': 56, 'T7': 20, 'T8': 24, 'T9': 17, 'T10': 28, 'Tp7': 19, 'Tp8': 23, 'P7': 12, 'P5': 36, 'P3': 23, 'P1': 36, 'Pz': 39, 'P2': 16, 'P4': 45, 'P6': 31, 'P8': 16, 'Po7': 19, 'Po3': 44, 'Poz': 24, 'Po4': 40, 'Po8': 14, 'O1': 34, 'Oz': 8, 'O2': 31, 'Iz': 24}

print('\x1b[1;35m'+'Top 10'+'\x1b[0m \n')
local_indegree_dict = IDX.local_indegree()
local_outdegree_dict = IDX.local_outdegree()
local_degree_dict = IDX.local_degree()


print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m\n')
print(npm.top10(local_indegree_dict))
print('\n\x1b[1;39m'+"Local outdegree: "+'\x1b[0m\n')
print(npm.top10(local_outdegree_dict))
print('\n\x1b[1;39m'+"Local degree: "+'\x1b[0m\n')
print(npm.top10(local_degree_dict))

Top 10 

Local indegree: 

{'Po3': 37, 'Po4': 36, 'P4': 34, 'P5': 29, 'O1': 28, 'O2': 27, 'T10': 26, 'Cp1': 25, 'P1': 25, 'Pz': 25}

Local outdegree: 

{'Ft8': 56, 'Af8': 41, 'Af3': 35, 'Af7': 32, 'Fpz': 26, 'Fp2': 25, 'F3': 25, 'F5': 23, 'Fp1': 22, 'Af4': 22}

Local degree: 

{'Ft8': 56, 'P4': 45, 'Po3': 44, 'Af8': 41, 'Po4': 40, 'Pz': 39, 'Af7': 38, 'Fz': 38, 'P5': 36, 'P1': 36}

Closed Eyes

IDX = npm.Graph_Theory_Indices("PDC")
IDX.import_data(closed_eyes)
IDX.connectivity(freq=freq_alpha, threshold=0.2, plot=False,order=order_closed)

print('\x1b[1;35m'+'\nGlobal indices'+'\x1b[0m \n')
print("APL (Average Path Length) and GCC (Global Clustering Coefficient)\n")
print('\x1b[1;39m'+"APL (w/o weights): "+'\x1b[0m', IDX.average_path_length(False))
print('\x1b[1;39m'+"GCC (w/o weights): "+'\x1b[0m', IDX.global_clustering_coefficient(False))
print("\n")
print('\x1b[1;35m'+'Local indices'+'\x1b[0m \n')
print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m', IDX.local_indegree())
print('\x1b[1;39m'+"Local outdegree: "+'\x1b[0m', IDX.local_indegree())
print('\x1b[1;39m'+"Local degree: "+'\x1b[0m', IDX.local_indegree())

Global indices 

APL (Average Path Length) and GCC (Global Clustering Coefficient)

APL (w/o weights):  2.175843253968254
GCC (w/o weights):  0.2976075168226443


Local indices 

Local indegree:  {'Fc5': 8, 'Fc3': 15, 'Fc1': 5, 'Fcz': 29, 'Fc2': 39, 'Fc4': 24, 'Fc6': 34, 'C5': 2, 'C3': 3, 'C1': 9, 'Cz': 14, 'C2': 7, 'C4': 4, 'C6': 3, 'Cp5': 0, 'Cp3': 11, 'Cp1': 13, 'Cpz': 10, 'Cp2': 15, 'Cp4': 0, 'Cp6': 11, 'Fp1': 5, 'Fpz': 3, 'Fp2': 5, 'Af7': 1, 'Af3': 0, 'Afz': 3, 'Af4': 9, 'Af8': 1, 'F7': 5, 'F5': 2, 'F3': 4, 'F1': 5, 'Fz': 20, 'F2': 30, 'F4': 32, 'F6': 3, 'F8': 5, 'Ft7': 10, 'Ft8': 0, 'T7': 12, 'T8': 9, 'T9': 2, 'T10': 31, 'Tp7': 20, 'Tp8': 12, 'P7': 14, 'P5': 15, 'P3': 26, 'P1': 16, 'Pz': 21, 'P2': 0, 'P4': 19, 'P6': 15, 'P8': 16, 'Po7': 24, 'Po3': 21, 'Poz': 14, 'Po4': 19, 'Po8': 12, 'O1': 25, 'Oz': 20, 'O2': 19, 'Iz': 25}
Local outdegree:  {'Fc5': 8, 'Fc3': 15, 'Fc1': 5, 'Fcz': 29, 'Fc2': 39, 'Fc4': 24, 'Fc6': 34, 'C5': 2, 'C3': 3, 'C1': 9, 'Cz': 14, 'C2': 7, 'C4': 4, 'C6': 3, 'Cp5': 0, 'Cp3': 11, 'Cp1': 13, 'Cpz': 10, 'Cp2': 15, 'Cp4': 0, 'Cp6': 11, 'Fp1': 5, 'Fpz': 3, 'Fp2': 5, 'Af7': 1, 'Af3': 0, 'Afz': 3, 'Af4': 9, 'Af8': 1, 'F7': 5, 'F5': 2, 'F3': 4, 'F1': 5, 'Fz': 20, 'F2': 30, 'F4': 32, 'F6': 3, 'F8': 5, 'Ft7': 10, 'Ft8': 0, 'T7': 12, 'T8': 9, 'T9': 2, 'T10': 31, 'Tp7': 20, 'Tp8': 12, 'P7': 14, 'P5': 15, 'P3': 26, 'P1': 16, 'Pz': 21, 'P2': 0, 'P4': 19, 'P6': 15, 'P8': 16, 'Po7': 24, 'Po3': 21, 'Poz': 14, 'Po4': 19, 'Po8': 12, 'O1': 25, 'Oz': 20, 'O2': 19, 'Iz': 25}
Local degree:  {'Fc5': 8, 'Fc3': 15, 'Fc1': 5, 'Fcz': 29, 'Fc2': 39, 'Fc4': 24, 'Fc6': 34, 'C5': 2, 'C3': 3, 'C1': 9, 'Cz': 14, 'C2': 7, 'C4': 4, 'C6': 3, 'Cp5': 0, 'Cp3': 11, 'Cp1': 13, 'Cpz': 10, 'Cp2': 15, 'Cp4': 0, 'Cp6': 11, 'Fp1': 5, 'Fpz': 3, 'Fp2': 5, 'Af7': 1, 'Af3': 0, 'Afz': 3, 'Af4': 9, 'Af8': 1, 'F7': 5, 'F5': 2, 'F3': 4, 'F1': 5, 'Fz': 20, 'F2': 30, 'F4': 32, 'F6': 3, 'F8': 5, 'Ft7': 10, 'Ft8': 0, 'T7': 12, 'T8': 9, 'T9': 2, 'T10': 31, 'Tp7': 20, 'Tp8': 12, 'P7': 14, 'P5': 15, 'P3': 26, 'P1': 16, 'Pz': 21, 'P2': 0, 'P4': 19, 'P6': 15, 'P8': 16, 'Po7': 24, 'Po3': 21, 'Poz': 14, 'Po4': 19, 'Po8': 12, 'O1': 25, 'Oz': 20, 'O2': 19, 'Iz': 25}

print('\x1b[1;35m'+'Top 10'+'\x1b[0m \n')
local_indegree_dict = IDX.local_indegree()
local_outdegree_dict = IDX.local_outdegree()
local_degree_dict = IDX.local_degree()


print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m\n')
print(npm.top10(local_indegree_dict))
print('\n\x1b[1;39m'+"Local outdegree: "+'\x1b[0m\n')
print(npm.top10(local_outdegree_dict))
print('\n\x1b[1;39m'+"Local degree: "+'\x1b[0m\n')
print(npm.top10(local_degree_dict))

Top 10 

Local indegree: 

{'Fc2': 39, 'Fc6': 34, 'F4': 32, 'T10': 31, 'F2': 30, 'Fcz': 29, 'P3': 26, 'O1': 25, 'Iz': 25, 'Fc4': 24}

Local outdegree: 

{'Af7': 32, 'F7': 28, 'F5': 28, 'Af3': 24, 'F3': 22, 'Fp1': 21, 'F1': 19, 'O1': 18, 'Fpz': 17, 'Ft7': 17}

Local degree: 

{'Fc2': 46, 'O1': 43, 'F4': 42, 'Fc6': 39, 'F2': 38, 'Oz': 37, 'Fcz': 36, 'Pz': 36, 'Po4': 35, 'P3': 34}

2.2. (class ‘D’)

Search in the literature a definition of small-worldness index (i.e. an index describing the small-world organization of a network) and compute it.

Open Eyes

IDX = npm.Graph_Theory_Indices("PDC")
IDX.import_data(open_eyes)
IDX.connectivity(freq=freq_alpha, threshold=0.2, plot=False,order=order_open)

C = IDX.global_clustering_coefficient(False)
L = IDX.average_path_length(False)

print('\x1b[1;35m'+'\nAverage Path Length and Global Clustering Coefficient for\nGraph\n' + '\x1b[0m')
print('\x1b[1;39m'+ 'L '+'\x1b[0m' + '= ' + str(L),'\x1b[1;39m'+ '    C '+'\x1b[0m' + '= ' + str(C) )


APL_rnd_list = []
GCC_rnd_list = []

for cnt in range(10):
    G_r_Adj = np.array([r for r in IDX.G.get_adjacency()])
    G_r_Adj = bct.randmio_dir(G_r_Adj, 10)[0]
    G_r_Adj = [list(r) for r in G_r_Adj]
    G_r_Adj = [[G_r_Adj[i][j].item() for j in range(len(G_r_Adj))] for i in range(len(G_r_Adj))]
    G_r = Graph.Adjacency(G_r_Adj)
    APL_rnd_list.append(npm.average_path_length(G_r, False))
    GCC_rnd_list.append(npm.global_clustering_coefficient(G_r, False))
    
    
L_r = np.mean(APL_rnd_list)
C_r = np.mean(GCC_rnd_list)

print('\x1b[1;35m'+'\nAverage Path Length and Global Clustering Coefficient for\nEquivalent Random Graph\n' + '\x1b[0m')
print('\x1b[1;39m'+ 'L_r '+'\x1b[0m' + '= ' + str(L_r),'\x1b[1;39m'+ '    C_r '+'\x1b[0m' + '= ' + str(C_r) )

APL_ltc_list = []
GCC_ltc_list = []

for cnt in range(10):
    
    G_l_Adj = np.array([r for r in IDX.G.get_adjacency()])
    G_l_Adj = bct.latmio_dir(G_l_Adj, 10)[0]
    G_l_Adj = [list(r) for r in G_l_Adj]
    G_l_Adj = [[G_l_Adj[i][j].item() for j in range(len(G_r_Adj))] for i in range(len(G_r_Adj))]
    G_l = Graph.Adjacency(G_l_Adj)
    APL_ltc_list.append(npm.average_path_length(G_l, False))
    GCC_ltc_list.append(npm.global_clustering_coefficient(G_l, False))
    
L_l = np.mean(APL_ltc_list)
C_l = np.mean(GCC_ltc_list)

print('\x1b[1;35m'+'\nAverage Path Length and Global Clustering Coefficient for\nEquivalent Lattice Graph\n' + '\x1b[0m')
print('\x1b[1;39m'+ 'L_l '+'\x1b[0m' + '= ' + str(L_l),'\x1b[1;39m'+ '    C_l '+'\x1b[0m' + '= ' + str(C_l) )

SWI = (L - L_l)/(L_r - L_l) * (C - C_r)/(C_l - C_r)

print('\x1b[1;35m'+'\nSmall World Index\n' + '\x1b[0m')
print('\x1b[1;39m'+ 'SWI '+'\x1b[0m' + '=', SWI)

Average Path Length and Global Clustering Coefficient for
Graph

L = 1.5830853174603174     C = 0.2703346932759426

Average Path Length and Global Clustering Coefficient for
Equivalent Random Graph

L_r = 1.8431547619047617     C_r = 0.22469864000462647

Average Path Length and Global Clustering Coefficient for
Equivalent Lattice Graph

L_l = 2.155406746031746     C_l = 0.37621787054326666

Small World Index

SWI = 0.5520458156620558

Closed Eyes

IDX = npm.Graph_Theory_Indices("PDC")
IDX.import_data(closed_eyes)
IDX.connectivity(freq=freq_alpha, threshold=0.2, plot=False,order=order_closed)

C = IDX.global_clustering_coefficient(False)
L = IDX.average_path_length(False)

print('\x1b[1;35m'+'\nAverage Path Length and Global Clustering Coefficient for\nGraph\n' + '\x1b[0m')
print('\x1b[1;39m'+ 'L '+'\x1b[0m' + '= ' + str(L),'\x1b[1;39m'+ '    C '+'\x1b[0m' + '= ' + str(C) )


APL_rnd_list = []
GCC_rnd_list = []

for cnt in range(10):
    G_r_Adj = np.array([r for r in IDX.G.get_adjacency()])
    G_r_Adj = bct.randmio_dir(G_r_Adj, 10)[0]
    G_r_Adj = [list(r) for r in G_r_Adj]
    G_r_Adj = [[G_r_Adj[i][j].item() for j in range(len(G_r_Adj))] for i in range(len(G_r_Adj))]
    G_r = Graph.Adjacency(G_r_Adj)
    APL_rnd_list.append(npm.average_path_length(G_r, False))
    GCC_rnd_list.append(npm.global_clustering_coefficient(G_r, False))
    
    
L_r = np.mean(APL_rnd_list)
C_r = np.mean(GCC_rnd_list)

print('\x1b[1;35m'+'\nAverage Path Length and Global Clustering Coefficient for\nEquivalent Random Graph\n' + '\x1b[0m')
print('\x1b[1;39m'+ 'L_r '+'\x1b[0m' + '= ' + str(L_r),'\x1b[1;39m'+ '    C_r '+'\x1b[0m' + '= ' + str(C_r) )

APL_ltc_list = []
GCC_ltc_list = []

for cnt in range(10):
    
    G_l_Adj = np.array([r for r in IDX.G.get_adjacency()])
    G_l_Adj = bct.latmio_dir(G_l_Adj, 10)[0]
    G_l_Adj = [list(r) for r in G_l_Adj]
    G_l_Adj = [[G_l_Adj[i][j].item() for j in range(len(G_r_Adj))] for i in range(len(G_r_Adj))]
    G_l = Graph.Adjacency(G_l_Adj)
    APL_ltc_list.append(npm.average_path_length(G_l, False))
    GCC_ltc_list.append(npm.global_clustering_coefficient(G_l, False))
    
L_l = np.mean(APL_ltc_list)
C_l = np.mean(GCC_ltc_list)

print('\x1b[1;35m'+'\nAverage Path Length and Global Clustering Coefficient for\nEquivalent Lattice Graph\n' + '\x1b[0m')
print('\x1b[1;39m'+ 'L_l '+'\x1b[0m' + '= ' + str(L_l),'\x1b[1;39m'+ '    C_l '+'\x1b[0m' + '= ' + str(C_l) )

SWI = (L - L_l)/(L_r - L_l) * (C - C_r)/(C_l - C_r)

print('\x1b[1;35m'+'\nSmall World Index\n' + '\x1b[0m')
print('\x1b[1;39m'+ 'SWI '+'\x1b[0m' + '=', SWI)

Average Path Length and Global Clustering Coefficient for
Graph

L = 2.175843253968254     C = 0.2976075168226443

Average Path Length and Global Clustering Coefficient for
Equivalent Random Graph

L_r = 1.7603918650793653     C_r = 0.21401572885041853

Average Path Length and Global Clustering Coefficient for
Equivalent Lattice Graph

L_l = 2.256423611111111     C_l = 0.4174545802608406

Small World Index

SWI = 0.06674971797148273

2.3. (class ‘B’)

Compare the global indices extracted from PDC and DTF connectivity estimations.

Open Eyes

IDX = npm.Graph_Theory_Indices("DTF")
IDX.import_data(open_eyes)
IDX.connectivity(freq=freq_alpha, threshold=0.2, plot=False,order=order_open)

print('\x1b[1;35m'+'\nGlobal indices'+'\x1b[0m \n')
print("APL (Average Path Length) and GCC (Global Clustering Coefficient)\n")
print('\x1b[1;39m'+"APL (w/o weights): "+'\x1b[0m', IDX.average_path_length(False))
print('\x1b[1;39m'+"GCC (w/o weights): "+'\x1b[0m', IDX.global_clustering_coefficient(False))
print("\n")
print('\x1b[1;35m'+'Local indices'+'\x1b[0m \n')
print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m', IDX.local_indegree())
print('\x1b[1;39m'+"Local outdegree: "+'\x1b[0m', IDX.local_outdegree())
print('\x1b[1;39m'+"Local degree: "+'\x1b[0m', IDX.local_degree())

Global indices 

APL (Average Path Length) and GCC (Global Clustering Coefficient)

APL (w/o weights):  0.9064980158730158
GCC (w/o weights):  0.4616604825678823


Local indices 

Local indegree:  {'Fc5': 2, 'Fc3': 27, 'Fc1': 0, 'Fcz': 21, 'Fc2': 24, 'Fc4': 56, 'Fc6': 41, 'C5': 0, 'C3': 3, 'C1': 2, 'Cz': 1, 'C2': 3, 'C4': 0, 'C6': 0, 'Cp5': 0, 'Cp3': 0, 'Cp1': 58, 'Cpz': 2, 'Cp2': 13, 'Cp4': 0, 'Cp6': 22, 'Fp1': 0, 'Fpz': 0, 'Fp2': 0, 'Af7': 0, 'Af3': 0, 'Afz': 0, 'Af4': 0, 'Af8': 0, 'F7': 0, 'F5': 0, 'F3': 0, 'F1': 1, 'Fz': 12, 'F2': 31, 'F4': 23, 'F6': 0, 'F8': 0, 'Ft7': 1, 'Ft8': 0, 'T7': 1, 'T8': 3, 'T9': 0, 'T10': 61, 'Tp7': 13, 'Tp8': 21, 'P7': 0, 'P5': 38, 'P3': 7, 'P1': 14, 'Pz': 7, 'P2': 0, 'P4': 12, 'P6': 2, 'P8': 61, 'Po7': 7, 'Po3': 54, 'Poz': 5, 'Po4': 31, 'Po8': 16, 'O1': 17, 'Oz': 1, 'O2': 35, 'Iz': 57}
Local outdegree:  {'Fc5': 13, 'Fc3': 13, 'Fc1': 16, 'Fcz': 18, 'Fc2': 16, 'Fc4': 10, 'Fc6': 10, 'C5': 12, 'C3': 14, 'C1': 20, 'Cz': 16, 'C2': 16, 'C4': 14, 'C6': 14, 'Cp5': 13, 'Cp3': 13, 'Cp1': 17, 'Cpz': 18, 'Cp2': 17, 'Cp4': 11, 'Cp6': 17, 'Fp1': 14, 'Fpz': 12, 'Fp2': 11, 'Af7': 12, 'Af3': 14, 'Afz': 9, 'Af4': 16, 'Af8': 17, 'F7': 12, 'F5': 14, 'F3': 14, 'F1': 10, 'Fz': 12, 'F2': 8, 'F4': 9, 'F6': 17, 'F8': 13, 'Ft7': 14, 'Ft8': 12, 'T7': 13, 'T8': 13, 'T9': 14, 'T10': 3, 'Tp7': 9, 'Tp8': 14, 'P7': 12, 'P5': 15, 'P3': 12, 'P1': 9, 'Pz': 12, 'P2': 7, 'P4': 7, 'P6': 9, 'P8': 12, 'Po7': 15, 'Po3': 10, 'Poz': 10, 'Po4': 6, 'Po8': 9, 'O1': 12, 'Oz': 11, 'O2': 14, 'Iz': 10}
Local degree:  {'Fc5': 15, 'Fc3': 40, 'Fc1': 16, 'Fcz': 39, 'Fc2': 40, 'Fc4': 66, 'Fc6': 51, 'C5': 12, 'C3': 17, 'C1': 22, 'Cz': 17, 'C2': 19, 'C4': 14, 'C6': 14, 'Cp5': 13, 'Cp3': 13, 'Cp1': 75, 'Cpz': 20, 'Cp2': 30, 'Cp4': 11, 'Cp6': 39, 'Fp1': 14, 'Fpz': 12, 'Fp2': 11, 'Af7': 12, 'Af3': 14, 'Afz': 9, 'Af4': 16, 'Af8': 17, 'F7': 12, 'F5': 14, 'F3': 14, 'F1': 11, 'Fz': 24, 'F2': 39, 'F4': 32, 'F6': 17, 'F8': 13, 'Ft7': 15, 'Ft8': 12, 'T7': 14, 'T8': 16, 'T9': 14, 'T10': 64, 'Tp7': 22, 'Tp8': 35, 'P7': 12, 'P5': 53, 'P3': 19, 'P1': 23, 'Pz': 19, 'P2': 7, 'P4': 19, 'P6': 11, 'P8': 73, 'Po7': 22, 'Po3': 64, 'Poz': 15, 'Po4': 37, 'Po8': 25, 'O1': 29, 'Oz': 12, 'O2': 49, 'Iz': 67}

print('\x1b[1;35m'+'Top 10'+'\x1b[0m \n')
local_indegree_dict = IDX.local_indegree()
local_outdegree_dict = IDX.local_outdegree()
local_degree_dict = IDX.local_degree()


print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m\n')
print(npm.top10(local_indegree_dict))
print('\n\x1b[1;39m'+"Local outdegree: "+'\x1b[0m\n')
print(npm.top10(local_outdegree_dict))
print('\n\x1b[1;39m'+"Local degree: "+'\x1b[0m\n')
print(npm.top10(local_degree_dict))

Top 10 

Local indegree: 

{'T10': 61, 'P8': 61, 'Cp1': 58, 'Iz': 57, 'Fc4': 56, 'Po3': 54, 'Fc6': 41, 'P5': 38, 'O2': 35, 'F2': 31}

Local outdegree: 

{'C1': 20, 'Fcz': 18, 'Cpz': 18, 'Cp1': 17, 'Cp2': 17, 'Cp6': 17, 'Af8': 17, 'F6': 17, 'Fc1': 16, 'Fc2': 16}

Local degree: 

{'Cp1': 75, 'P8': 73, 'Iz': 67, 'Fc4': 66, 'T10': 64, 'Po3': 64, 'P5': 53, 'Fc6': 51, 'O2': 49, 'Fc3': 40}

Closed Eyes

IDX = npm.Graph_Theory_Indices("DTF")
IDX.import_data(closed_eyes)
IDX.connectivity(freq=freq_alpha, threshold=0.2, plot=False,order=order_closed)

print('\x1b[1;35m'+'\nGlobal indices'+'\x1b[0m \n')
print("APL (Average Path Length) and GCC (Global Clustering Coefficient)\n")
print('\x1b[1;39m'+"APL (w/o weights): "+'\x1b[0m', IDX.average_path_length(False))
print('\x1b[1;39m'+"GCC (w/o weights): "+'\x1b[0m', IDX.global_clustering_coefficient(False))
print("\n")
print('\x1b[1;35m'+'Local indices'+'\x1b[0m \n')
print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m', IDX.local_indegree())
print('\x1b[1;39m'+"Local outdegree: "+'\x1b[0m', IDX.local_outdegree())
print('\x1b[1;39m'+"Local degree: "+'\x1b[0m', IDX.local_degree())

Global indices 

APL (Average Path Length) and GCC (Global Clustering Coefficient)

APL (w/o weights):  0.4508928571428571
GCC (w/o weights):  0.5492694923305701


Local indices 

Local indegree:  {'Fc5': 0, 'Fc3': 0, 'Fc1': 0, 'Fcz': 27, 'Fc2': 46, 'Fc4': 48, 'Fc6': 63, 'C5': 0, 'C3': 0, 'C1': 0, 'Cz': 1, 'C2': 0, 'C4': 0, 'C6': 0, 'Cp5': 0, 'Cp3': 0, 'Cp1': 0, 'Cpz': 9, 'Cp2': 1, 'Cp4': 0, 'Cp6': 0, 'Fp1': 0, 'Fpz': 0, 'Fp2': 0, 'Af7': 0, 'Af3': 0, 'Afz': 0, 'Af4': 0, 'Af8': 0, 'F7': 0, 'F5': 0, 'F3': 0, 'F1': 0, 'Fz': 24, 'F2': 43, 'F4': 44, 'F6': 0, 'F8': 0, 'Ft7': 0, 'Ft8': 0, 'T7': 0, 'T8': 1, 'T9': 0, 'T10': 7, 'Tp7': 15, 'Tp8': 0, 'P7': 1, 'P5': 1, 'P3': 12, 'P1': 0, 'Pz': 5, 'P2': 0, 'P4': 31, 'P6': 0, 'P8': 35, 'Po7': 56, 'Po3': 44, 'Poz': 26, 'Po4': 43, 'Po8': 62, 'O1': 33, 'Oz': 13, 'O2': 56, 'Iz': 59}
Local outdegree:  {'Fc5': 12, 'Fc3': 12, 'Fc1': 11, 'Fcz': 11, 'Fc2': 10, 'Fc4': 12, 'Fc6': 13, 'C5': 13, 'C3': 14, 'C1': 14, 'Cz': 14, 'C2': 13, 'C4': 12, 'C6': 12, 'Cp5': 13, 'Cp3': 14, 'Cp1': 13, 'Cpz': 15, 'Cp2': 13, 'Cp4': 14, 'Cp6': 14, 'Fp1': 14, 'Fpz': 14, 'Fp2': 15, 'Af7': 12, 'Af3': 13, 'Afz': 15, 'Af4': 13, 'Af8': 15, 'F7': 12, 'F5': 11, 'F3': 13, 'F1': 12, 'Fz': 11, 'F2': 11, 'F4': 11, 'F6': 11, 'F8': 15, 'Ft7': 13, 'Ft8': 11, 'T7': 11, 'T8': 11, 'T9': 11, 'T10': 8, 'Tp7': 11, 'Tp8': 14, 'P7': 13, 'P5': 13, 'P3': 14, 'P1': 15, 'Pz': 14, 'P2': 13, 'P4': 13, 'P6': 12, 'P8': 11, 'Po7': 12, 'Po3': 14, 'Poz': 12, 'Po4': 11, 'Po8': 11, 'O1': 12, 'Oz': 13, 'O2': 13, 'Iz': 13}
Local degree:  {'Fc5': 12, 'Fc3': 12, 'Fc1': 11, 'Fcz': 38, 'Fc2': 56, 'Fc4': 60, 'Fc6': 76, 'C5': 13, 'C3': 14, 'C1': 14, 'Cz': 15, 'C2': 13, 'C4': 12, 'C6': 12, 'Cp5': 13, 'Cp3': 14, 'Cp1': 13, 'Cpz': 24, 'Cp2': 14, 'Cp4': 14, 'Cp6': 14, 'Fp1': 14, 'Fpz': 14, 'Fp2': 15, 'Af7': 12, 'Af3': 13, 'Afz': 15, 'Af4': 13, 'Af8': 15, 'F7': 12, 'F5': 11, 'F3': 13, 'F1': 12, 'Fz': 35, 'F2': 54, 'F4': 55, 'F6': 11, 'F8': 15, 'Ft7': 13, 'Ft8': 11, 'T7': 11, 'T8': 12, 'T9': 11, 'T10': 15, 'Tp7': 26, 'Tp8': 14, 'P7': 14, 'P5': 14, 'P3': 26, 'P1': 15, 'Pz': 19, 'P2': 13, 'P4': 44, 'P6': 12, 'P8': 46, 'Po7': 68, 'Po3': 58, 'Poz': 38, 'Po4': 54, 'Po8': 73, 'O1': 45, 'Oz': 26, 'O2': 69, 'Iz': 72}

print('\x1b[1;35m'+'Top 10'+'\x1b[0m \n')
local_indegree_dict = IDX.local_indegree()
local_outdegree_dict = IDX.local_outdegree()
local_degree_dict = IDX.local_degree()


print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m\n')
print(npm.top10(local_indegree_dict))
print('\n\x1b[1;39m'+"Local outdegree: "+'\x1b[0m\n')
print(npm.top10(local_outdegree_dict))
print('\n\x1b[1;39m'+"Local degree: "+'\x1b[0m\n')
print(npm.top10(local_degree_dict))

Top 10 

Local indegree: 

{'Fc6': 63, 'Po8': 62, 'Iz': 59, 'Po7': 56, 'O2': 56, 'Fc4': 48, 'Fc2': 46, 'F4': 44, 'Po3': 44, 'F2': 43}

Local outdegree: 

{'Cpz': 15, 'Fp2': 15, 'Afz': 15, 'Af8': 15, 'F8': 15, 'P1': 15, 'C3': 14, 'C1': 14, 'Cz': 14, 'Cp3': 14}

Local degree: 

{'Fc6': 76, 'Po8': 73, 'Iz': 72, 'O2': 69, 'Po7': 68, 'Fc4': 60, 'Po3': 58, 'Fc2': 56, 'F4': 55, 'F2': 54}

2.4.(class ‘C’)

Study the behaviour of global graph indices in function of network density (see point 2.3 for density values).

(the choice of this task is advised in the case of selection of task 1.3).

Open Eyes

random.seed (7890)
APL_list = []
GCC_list = []

densities = [0.01,0.05,0.1,0.2,0.3,0.5]

for d in tqdm_notebook(densities):
    
    IDX = npm.Graph_Theory_Indices("PDC")
    IDX.import_data(open_eyes)
    IDX.connectivity(freq=freq_alpha, threshold=d, plot=False,order=order_open)
    
    APL_list.append(IDX.average_path_length(False))
    GCC_list.append(IDX.global_clustering_coefficient(False))

plt.plot(densities, APL_list)

[<matplotlib.lines.Line2D at 0x1c28c94f60>]

plt.plot(densities, GCC_list)

[<matplotlib.lines.Line2D at 0x1c288b6550>]

Closed Eyes

APL_list = []
GCC_list = []

densities = [0.01,0.05,0.1,0.2,0.3,0.5]

for d in tqdm_notebook(densities):
    
    IDX = npm.Graph_Theory_Indices("PDC")
    IDX.import_data(closed_eyes)
    IDX.connectivity(freq=freq_alpha, threshold=d, plot=False,order=order_closed)
    
    APL_list.append(IDX.average_path_length(False))
    GCC_list.append(IDX.global_clustering_coefficient(False))

plt.plot(densities, APL_list)

[<matplotlib.lines.Line2D at 0x1c2873f390>]

plt.plot(densities, GCC_list)

[<matplotlib.lines.Line2D at 0x1c28c03400>]

2.5.(class ‘B’)

Make a topographical representation of local indices.

def plot_graph(G, name, degree_list):
        
        G.es['arrow_size'] = [0.1 for edge in G.es]
        visual_style = {}
        visual_style["vertex_size"] = degree_list*10
        visual_style["vertex_color"] = "red"
        visual_style["vertex_label"] = G.vs["label"]
        visual_style["edge_width"] = 0.2
        visual_style["layout"] = G.vs["coords"]
        
        graph = igraph.plot(G, bbox=(0, 0, 600, 600), **visual_style)
        graph.save(name + '.png')
        
        return(graph)

Open Eyes

IDX = npm.Graph_Theory_Indices("PDC")
IDX.import_data(open_eyes)
IDX.connectivity(freq=freq_alpha, threshold=0.2, plot=False,order=order_open)

degree_list = list(IDX.local_indegree().values())
plot_graph(IDX.G, "EO_indegree_graph", degree_list)

degree_list = list(IDX.local_outdegree().values())
plot_graph(IDX.G, "EO_outdegree_graph", degree_list)

degree_list = list(IDX.local_degree().values())
plot_graph(IDX.G, "EO_degree_graph", degree_list)

Closed Eyes

IDX = npm.Graph_Theory_Indices("PDC")
IDX.import_data(closed_eyes)
IDX.connectivity(freq=freq_alpha, threshold=0.2, plot=False,order=order_closed)

degree_list = list(IDX.local_indegree().values())
plot_graph(IDX.G, "EC_indegree_graph", degree_list)

degree_list = list(IDX.local_outdegree().values())
plot_graph(IDX.G, "EC_outdegree_graph", degree_list)

degree_list = list(IDX.local_degree().values())
plot_graph(IDX.G, "EC_degree_graph", degree_list)

2.6. (class ‘B’)

Compare the networks obtained with the analysis 1.6 in terms of graph indices.

(the choice of this task is advised only in the case of selection of task 1.6)

Open Eyes

IDX = npm.Graph_Theory_Indices("PDC")
IDX.import_data(open_eyes)
IDX.connectivity(freq=freq_beta, threshold=0.2, plot=False,order=order_open)

print('\x1b[1;35m'+'\nGlobal indices'+'\x1b[0m \n')
print("APL (Average Path Length) and GCC (Global Clustering Coefficient)\n")
print('\x1b[1;39m'+"APL (w/o weights): "+'\x1b[0m', IDX.average_path_length(False))
print('\x1b[1;39m'+"GCC (w/o weights): "+'\x1b[0m', IDX.global_clustering_coefficient(False))
print("\n")
print('\x1b[1;35m'+'Local indices'+'\x1b[0m \n')
print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m', IDX.local_indegree())
print('\x1b[1;39m'+"Local outdegree: "+'\x1b[0m', IDX.local_outdegree())
print('\x1b[1;39m'+"Local degree: "+'\x1b[0m', IDX.local_degree())

Global indices 

APL (Average Path Length) and GCC (Global Clustering Coefficient)

APL (w/o weights):  3.313988095238095
GCC (w/o weights):  0.286981038201807


Local indices 

Local indegree:  {'Fc5': 12, 'Fc3': 19, 'Fc1': 9, 'Fcz': 14, 'Fc2': 10, 'Fc4': 21, 'Fc6': 18, 'C5': 6, 'C3': 8, 'C1': 15, 'Cz': 21, 'C2': 22, 'C4': 4, 'C6': 3, 'Cp5': 13, 'Cp3': 6, 'Cp1': 11, 'Cpz': 3, 'Cp2': 7, 'Cp4': 4, 'Cp6': 27, 'Fp1': 7, 'Fpz': 5, 'Fp2': 2, 'Af7': 5, 'Af3': 2, 'Afz': 6, 'Af4': 5, 'Af8': 1, 'F7': 2, 'F5': 2, 'F3': 4, 'F1': 10, 'Fz': 25, 'F2': 12, 'F4': 21, 'F6': 3, 'F8': 4, 'Ft7': 15, 'Ft8': 0, 'T7': 17, 'T8': 28, 'T9': 4, 'T10': 27, 'Tp7': 13, 'Tp8': 9, 'P7': 2, 'P5': 23, 'P3': 13, 'P1': 17, 'Pz': 21, 'P2': 4, 'P4': 36, 'P6': 15, 'P8': 18, 'Po7': 22, 'Po3': 30, 'Poz': 6, 'Po4': 33, 'Po8': 2, 'O1': 15, 'Oz': 12, 'O2': 30, 'Iz': 25}
Local outdegree:  {'Fc5': 8, 'Fc3': 8, 'Fc1': 6, 'Fcz': 8, 'Fc2': 7, 'Fc4': 3, 'Fc6': 3, 'C5': 13, 'C3': 10, 'C1': 8, 'Cz': 7, 'C2': 8, 'C4': 8, 'C6': 5, 'Cp5': 6, 'Cp3': 6, 'Cp1': 8, 'Cpz': 12, 'Cp2': 5, 'Cp4': 5, 'Cp6': 3, 'Fp1': 23, 'Fpz': 29, 'Fp2': 30, 'Af7': 33, 'Af3': 39, 'Afz': 17, 'Af4': 20, 'Af8': 44, 'F7': 17, 'F5': 24, 'F3': 31, 'F1': 19, 'Fz': 19, 'F2': 4, 'F4': 7, 'F6': 21, 'F8': 15, 'Ft7': 12, 'Ft8': 59, 'T7': 9, 'T8': 5, 'T9': 12, 'T10': 0, 'Tp7': 9, 'Tp8': 8, 'P7': 12, 'P5': 10, 'P3': 12, 'P1': 11, 'Pz': 12, 'P2': 9, 'P4': 7, 'P6': 7, 'P8': 5, 'Po7': 9, 'Po3': 9, 'Poz': 10, 'Po4': 7, 'Po8': 6, 'O1': 10, 'Oz': 6, 'O2': 5, 'Iz': 6}
Local degree:  {'Fc5': 20, 'Fc3': 27, 'Fc1': 15, 'Fcz': 22, 'Fc2': 17, 'Fc4': 24, 'Fc6': 21, 'C5': 19, 'C3': 18, 'C1': 23, 'Cz': 28, 'C2': 30, 'C4': 12, 'C6': 8, 'Cp5': 19, 'Cp3': 12, 'Cp1': 19, 'Cpz': 15, 'Cp2': 12, 'Cp4': 9, 'Cp6': 30, 'Fp1': 30, 'Fpz': 34, 'Fp2': 32, 'Af7': 38, 'Af3': 41, 'Afz': 23, 'Af4': 25, 'Af8': 45, 'F7': 19, 'F5': 26, 'F3': 35, 'F1': 29, 'Fz': 44, 'F2': 16, 'F4': 28, 'F6': 24, 'F8': 19, 'Ft7': 27, 'Ft8': 59, 'T7': 26, 'T8': 33, 'T9': 16, 'T10': 27, 'Tp7': 22, 'Tp8': 17, 'P7': 14, 'P5': 33, 'P3': 25, 'P1': 28, 'Pz': 33, 'P2': 13, 'P4': 43, 'P6': 22, 'P8': 23, 'Po7': 31, 'Po3': 39, 'Poz': 16, 'Po4': 40, 'Po8': 8, 'O1': 25, 'Oz': 18, 'O2': 35, 'Iz': 31}

print('\x1b[1;35m'+'Top 10'+'\x1b[0m \n')
local_indegree_dict = IDX.local_indegree()
local_outdegree_dict = IDX.local_outdegree()
local_degree_dict = IDX.local_degree()


print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m\n')
print(npm.top10(local_indegree_dict))
print('\n\x1b[1;39m'+"Local outdegree: "+'\x1b[0m\n')
print(npm.top10(local_outdegree_dict))
print('\n\x1b[1;39m'+"Local degree: "+'\x1b[0m\n')
print(npm.top10(local_degree_dict))

Top 10 

Local indegree: 

{'P4': 36, 'Po4': 33, 'Po3': 30, 'O2': 30, 'T8': 28, 'Cp6': 27, 'T10': 27, 'Fz': 25, 'Iz': 25, 'P5': 23}

Local outdegree: 

{'Ft8': 59, 'Af8': 44, 'Af3': 39, 'Af7': 33, 'F3': 31, 'Fp2': 30, 'Fpz': 29, 'F5': 24, 'Fp1': 23, 'F6': 21}

Local degree: 

{'Ft8': 59, 'Af8': 45, 'Fz': 44, 'P4': 43, 'Af3': 41, 'Po4': 40, 'Po3': 39, 'Af7': 38, 'F3': 35, 'O2': 35}

Closed Eyes

IDX = npm.Graph_Theory_Indices("PDC")
IDX.import_data(closed_eyes)
IDX.connectivity(freq=freq_beta, threshold=0.2, plot=False,order=order_closed)

print('\x1b[1;35m'+'\nGlobal indices'+'\x1b[0m \n')
print("APL (Average Path Length) and GCC (Global Clustering Coefficient)\n")
print('\x1b[1;39m'+"APL (w/o weights): "+'\x1b[0m', IDX.average_path_length(False))
print('\x1b[1;39m'+"GCC (w/o weights): "+'\x1b[0m', IDX.global_clustering_coefficient(False))
print("\n")
print('\x1b[1;35m'+'Local indices'+'\x1b[0m \n')
print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m', IDX.local_indegree())
print('\x1b[1;39m'+"Local outdegree: "+'\x1b[0m', IDX.local_outdegree())
print('\x1b[1;39m'+"Local degree: "+'\x1b[0m', IDX.local_degree())

Global indices 

APL (Average Path Length) and GCC (Global Clustering Coefficient)

APL (w/o weights):  1.940972222222222
GCC (w/o weights):  0.2795460431477949


Local indices 

Local indegree:  {'Fc5': 9, 'Fc3': 9, 'Fc1': 3, 'Fcz': 20, 'Fc2': 24, 'Fc4': 25, 'Fc6': 33, 'C5': 1, 'C3': 3, 'C1': 15, 'Cz': 16, 'C2': 17, 'C4': 6, 'C6': 3, 'Cp5': 6, 'Cp3': 3, 'Cp1': 10, 'Cpz': 14, 'Cp2': 19, 'Cp4': 0, 'Cp6': 6, 'Fp1': 6, 'Fpz': 4, 'Fp2': 4, 'Af7': 1, 'Af3': 2, 'Afz': 2, 'Af4': 8, 'Af8': 0, 'F7': 2, 'F5': 1, 'F3': 3, 'F1': 5, 'Fz': 20, 'F2': 25, 'F4': 31, 'F6': 7, 'F8': 12, 'Ft7': 11, 'Ft8': 0, 'T7': 12, 'T8': 8, 'T9': 13, 'T10': 24, 'Tp7': 22, 'Tp8': 15, 'P7': 11, 'P5': 18, 'P3': 20, 'P1': 8, 'Pz': 17, 'P2': 0, 'P4': 17, 'P6': 14, 'P8': 16, 'Po7': 22, 'Po3': 28, 'Poz': 22, 'Po4': 18, 'Po8': 18, 'O1': 24, 'Oz': 19, 'O2': 33, 'Iz': 21}
Local outdegree:  {'Fc5': 8, 'Fc3': 12, 'Fc1': 6, 'Fcz': 7, 'Fc2': 8, 'Fc4': 3, 'Fc6': 3, 'C5': 14, 'C3': 11, 'C1': 9, 'Cz': 9, 'C2': 8, 'C4': 13, 'C6': 12, 'Cp5': 11, 'Cp3': 9, 'Cp1': 8, 'Cpz': 9, 'Cp2': 8, 'Cp4': 12, 'Cp6': 13, 'Fp1': 18, 'Fpz': 14, 'Fp2': 13, 'Af7': 29, 'Af3': 17, 'Afz': 15, 'Af4': 6, 'Af8': 15, 'F7': 29, 'F5': 30, 'F3': 22, 'F1': 20, 'Fz': 12, 'F2': 6, 'F4': 6, 'F6': 7, 'F8': 10, 'Ft7': 19, 'Ft8': 12, 'T7': 12, 'T8': 5, 'T9': 14, 'T10': 0, 'Tp7': 7, 'Tp8': 11, 'P7': 12, 'P5': 12, 'P3': 16, 'P1': 17, 'Pz': 17, 'P2': 20, 'P4': 18, 'P6': 16, 'P8': 13, 'Po7': 11, 'Po3': 14, 'Poz': 20, 'Po4': 12, 'Po8': 13, 'O1': 16, 'Oz': 15, 'O2': 14, 'Iz': 8}
Local degree:  {'Fc5': 17, 'Fc3': 21, 'Fc1': 9, 'Fcz': 27, 'Fc2': 32, 'Fc4': 28, 'Fc6': 36, 'C5': 15, 'C3': 14, 'C1': 24, 'Cz': 25, 'C2': 25, 'C4': 19, 'C6': 15, 'Cp5': 17, 'Cp3': 12, 'Cp1': 18, 'Cpz': 23, 'Cp2': 27, 'Cp4': 12, 'Cp6': 19, 'Fp1': 24, 'Fpz': 18, 'Fp2': 17, 'Af7': 30, 'Af3': 19, 'Afz': 17, 'Af4': 14, 'Af8': 15, 'F7': 31, 'F5': 31, 'F3': 25, 'F1': 25, 'Fz': 32, 'F2': 31, 'F4': 37, 'F6': 14, 'F8': 22, 'Ft7': 30, 'Ft8': 12, 'T7': 24, 'T8': 13, 'T9': 27, 'T10': 24, 'Tp7': 29, 'Tp8': 26, 'P7': 23, 'P5': 30, 'P3': 36, 'P1': 25, 'Pz': 34, 'P2': 20, 'P4': 35, 'P6': 30, 'P8': 29, 'Po7': 33, 'Po3': 42, 'Poz': 42, 'Po4': 30, 'Po8': 31, 'O1': 40, 'Oz': 34, 'O2': 47, 'Iz': 29}

print('\x1b[1;35m'+'Top 10'+'\x1b[0m \n')
local_indegree_dict = IDX.local_indegree()
local_outdegree_dict = IDX.local_outdegree()
local_degree_dict = IDX.local_degree()


print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m\n')
print(npm.top10(local_indegree_dict))
print('\n\x1b[1;39m'+"Local outdegree: "+'\x1b[0m\n')
print(npm.top10(local_outdegree_dict))
print('\n\x1b[1;39m'+"Local degree: "+'\x1b[0m\n')
print(npm.top10(local_degree_dict))

Top 10 

Local indegree: 

{'Fc6': 33, 'O2': 33, 'F4': 31, 'Po3': 28, 'Fc4': 25, 'F2': 25, 'Fc2': 24, 'T10': 24, 'O1': 24, 'Tp7': 22}

Local outdegree: 

{'F5': 30, 'Af7': 29, 'F7': 29, 'F3': 22, 'F1': 20, 'P2': 20, 'Poz': 20, 'Ft7': 19, 'Fp1': 18, 'P4': 18}

Local degree: 

{'O2': 47, 'Po3': 42, 'Poz': 42, 'O1': 40, 'F4': 37, 'Fc6': 36, 'P3': 36, 'P4': 35, 'Pz': 34, 'Oz': 34}

2.7. (class ‘C’)

Perform point 2.1 considering the weighted version of the graph indices definitions.

Open Eyes

IDX = npm.Graph_Theory_Indices("PDC")
IDX.import_data(open_eyes)
IDX.connectivity(freq=freq_alpha, threshold=0.2, plot=False,order=order_open)

print('\x1b[1;35m'+'\nGlobal indices'+'\x1b[0m \n')
print("APL (Average Path Length) and GCC (Global Clustering Coefficient)\n")
print('\x1b[1;39m'+"APL (w/ weights): "+'\x1b[0m', IDX.average_path_length(True))
print('\x1b[1;39m'+"GCC (w/ weights): "+'\x1b[0m', IDX.global_clustering_coefficient(True))
print("\n")
print('\x1b[1;35m'+'Local indices'+'\x1b[0m \n')
print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m', IDX.local_indegree(True))
print('\x1b[1;39m'+"Local outdegree: "+'\x1b[0m', IDX.local_indegree(True))
print('\x1b[1;39m'+"Local degree: "+'\x1b[0m', IDX.local_indegree(True))

Global indices 

APL (Average Path Length) and GCC (Global Clustering Coefficient)

APL (w/ weights):  0.20588908815171106
GCC (w/ weights):  0.04364233218120603


Local indices 

Local indegree:  {'Fc5': 1.957, 'Fc3': 3.096, 'Fc1': 2.14, 'Fcz': 3.296, 'Fc2': 2.797, 'Fc4': 3.369, 'Fc6': 3.709, 'C5': 0.591, 'C3': 1.579, 'C1': 2.004, 'Cz': 1.043, 'C2': 1.879, 'C4': 1.416, 'C6': 0.87, 'Cp5': 0.85, 'Cp3': 1.059, 'Cp1': 3.476, 'Cpz': 1.394, 'Cp2': 1.478, 'Cp4': 0.35, 'Cp6': 3.074, 'Fp1': 1.501, 'Fpz': 0.588, 'Fp2': 0.14, 'Af7': 0.966, 'Af3': 0.0, 'Afz': 1.191, 'Af4': 0.573, 'Af8': 0.0, 'F7': 0.528, 'F5': 0.265, 'F3': 1.144, 'F1': 1.744, 'Fz': 3.875, 'F2': 2.634, 'F4': 3.483, 'F6': 0.164, 'F8': 0.686, 'Ft7': 2.311, 'Ft8': 0.0, 'T7': 1.533, 'T8': 2.598, 'T9': 0.995, 'T10': 3.947, 'Tp7': 1.866, 'Tp8': 2.619, 'P7': 0.481, 'P5': 4.365, 'P3': 1.965, 'P1': 3.743, 'Pz': 4.001, 'P2': 0.587, 'P4': 4.905, 'P6': 3.471, 'P8': 2.064, 'Po7': 1.471, 'Po3': 5.323, 'Poz': 2.254, 'Po4': 4.887, 'Po8': 2.158, 'O1': 3.779, 'Oz': 1.109, 'O2': 3.786, 'Iz': 3.451}
Local outdegree:  {'Fc5': 1.957, 'Fc3': 3.096, 'Fc1': 2.14, 'Fcz': 3.296, 'Fc2': 2.797, 'Fc4': 3.369, 'Fc6': 3.709, 'C5': 0.591, 'C3': 1.579, 'C1': 2.004, 'Cz': 1.043, 'C2': 1.879, 'C4': 1.416, 'C6': 0.87, 'Cp5': 0.85, 'Cp3': 1.059, 'Cp1': 3.476, 'Cpz': 1.394, 'Cp2': 1.478, 'Cp4': 0.35, 'Cp6': 3.074, 'Fp1': 1.501, 'Fpz': 0.588, 'Fp2': 0.14, 'Af7': 0.966, 'Af3': 0.0, 'Afz': 1.191, 'Af4': 0.573, 'Af8': 0.0, 'F7': 0.528, 'F5': 0.265, 'F3': 1.144, 'F1': 1.744, 'Fz': 3.875, 'F2': 2.634, 'F4': 3.483, 'F6': 0.164, 'F8': 0.686, 'Ft7': 2.311, 'Ft8': 0.0, 'T7': 1.533, 'T8': 2.598, 'T9': 0.995, 'T10': 3.947, 'Tp7': 1.866, 'Tp8': 2.619, 'P7': 0.481, 'P5': 4.365, 'P3': 1.965, 'P1': 3.743, 'Pz': 4.001, 'P2': 0.587, 'P4': 4.905, 'P6': 3.471, 'P8': 2.064, 'Po7': 1.471, 'Po3': 5.323, 'Poz': 2.254, 'Po4': 4.887, 'Po8': 2.158, 'O1': 3.779, 'Oz': 1.109, 'O2': 3.786, 'Iz': 3.451}
Local degree:  {'Fc5': 1.957, 'Fc3': 3.096, 'Fc1': 2.14, 'Fcz': 3.296, 'Fc2': 2.797, 'Fc4': 3.369, 'Fc6': 3.709, 'C5': 0.591, 'C3': 1.579, 'C1': 2.004, 'Cz': 1.043, 'C2': 1.879, 'C4': 1.416, 'C6': 0.87, 'Cp5': 0.85, 'Cp3': 1.059, 'Cp1': 3.476, 'Cpz': 1.394, 'Cp2': 1.478, 'Cp4': 0.35, 'Cp6': 3.074, 'Fp1': 1.501, 'Fpz': 0.588, 'Fp2': 0.14, 'Af7': 0.966, 'Af3': 0.0, 'Afz': 1.191, 'Af4': 0.573, 'Af8': 0.0, 'F7': 0.528, 'F5': 0.265, 'F3': 1.144, 'F1': 1.744, 'Fz': 3.875, 'F2': 2.634, 'F4': 3.483, 'F6': 0.164, 'F8': 0.686, 'Ft7': 2.311, 'Ft8': 0.0, 'T7': 1.533, 'T8': 2.598, 'T9': 0.995, 'T10': 3.947, 'Tp7': 1.866, 'Tp8': 2.619, 'P7': 0.481, 'P5': 4.365, 'P3': 1.965, 'P1': 3.743, 'Pz': 4.001, 'P2': 0.587, 'P4': 4.905, 'P6': 3.471, 'P8': 2.064, 'Po7': 1.471, 'Po3': 5.323, 'Poz': 2.254, 'Po4': 4.887, 'Po8': 2.158, 'O1': 3.779, 'Oz': 1.109, 'O2': 3.786, 'Iz': 3.451}

print('\x1b[1;35m'+'Top 10'+'\x1b[0m \n')
local_indegree_dict = IDX.local_indegree(True)
local_outdegree_dict = IDX.local_outdegree(True)
local_degree_dict = IDX.local_degree(True)


print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m\n')
print(npm.top10(local_indegree_dict))
print('\n\x1b[1;39m'+"Local outdegree: "+'\x1b[0m\n')
print(npm.top10(local_outdegree_dict))
print('\n\x1b[1;39m'+"Local degree: "+'\x1b[0m\n')
print(npm.top10(local_degree_dict))

Top 10 

Local indegree: 

{'Po3': 5.323, 'P4': 4.905, 'Po4': 4.887, 'P5': 4.365, 'Pz': 4.001, 'T10': 3.947, 'Fz': 3.875, 'O2': 3.786, 'O1': 3.779, 'P1': 3.743}

Local outdegree: 

{'Ft8': 18.339, 'Af8': 8.971, 'Af3': 6.946, 'Af7': 4.817, 'Fpz': 4.191, 'Fp2': 4.185, 'F3': 3.616, 'F5': 3.432, 'Af4': 3.261, 'Fp1': 3.057}

Local degree: 

{'Ft8': 18.339, 'Af8': 8.971, 'Af3': 6.946, 'P4': 6.304, 'Fz': 6.245, 'Po3': 6.151, 'Pz': 5.807, 'Af7': 5.784, 'Po4': 5.401, 'P5': 5.366}

Closed Eyes

IDX = npm.Graph_Theory_Indices("PDC")
IDX.import_data(closed_eyes)
IDX.connectivity(freq=freq_alpha, threshold=0.2, plot=False,order=order_open)

print('\x1b[1;35m'+'\nGlobal indices'+'\x1b[0m \n')
print("APL (Average Path Length) and GCC (Global Clustering Coefficient)\n")
print('\x1b[1;39m'+"APL (w/ weights): "+'\x1b[0m', IDX.average_path_length(True))
print('\x1b[1;39m'+"GCC (w/ weights): "+'\x1b[0m', IDX.global_clustering_coefficient(True))
print("\n")
print('\x1b[1;35m'+'Local indices'+'\x1b[0m \n')
print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m', IDX.local_indegree(True))
print('\x1b[1;39m'+"Local outdegree: "+'\x1b[0m', IDX.local_indegree(True))
print('\x1b[1;39m'+"Local degree: "+'\x1b[0m', IDX.local_indegree(True))

Global indices 

APL (Average Path Length) and GCC (Global Clustering Coefficient)

APL (w/ weights):  0.3107543480489508
GCC (w/ weights):  0.049220312561394024


Local indices 

Local indegree:  {'Fc5': 1.461, 'Fc3': 2.07, 'Fc1': 1.014, 'Fcz': 5.036, 'Fc2': 5.072, 'Fc4': 4.133, 'Fc6': 5.154, 'C5': 0.256, 'C3': 1.15, 'C1': 0.634, 'Cz': 2.431, 'C2': 1.105, 'C4': 0.762, 'C6': 0.445, 'Cp5': 0.0, 'Cp3': 0.939, 'Cp1': 1.809, 'Cpz': 2.165, 'Cp2': 2.404, 'Cp4': 0.0, 'Cp6': 1.315, 'Fp1': 0.763, 'Fpz': 0.523, 'Fp2': 0.751, 'Af7': 0.169, 'Af3': 0.116, 'Afz': 0.577, 'Af4': 1.51, 'Af8': 0.121, 'F7': 0.85, 'F5': 0.368, 'F3': 0.718, 'F1': 0.686, 'Fz': 3.471, 'F2': 4.593, 'F4': 4.821, 'F6': 0.472, 'F8': 1.123, 'Ft7': 1.503, 'Ft8': 0.0, 'T7': 1.702, 'T8': 0.895, 'T9': 0.279, 'T10': 4.22, 'Tp7': 3.969, 'Tp8': 2.735, 'P7': 1.37, 'P5': 2.504, 'P3': 3.925, 'P1': 2.031, 'Pz': 3.767, 'P2': 0.231, 'P4': 3.575, 'P6': 1.963, 'P8': 2.699, 'Po7': 3.391, 'Po3': 3.863, 'Poz': 2.032, 'Po4': 3.586, 'Po8': 2.826, 'O1': 3.471, 'Oz': 2.862, 'O2': 2.297, 'Iz': 3.407}
Local outdegree:  {'Fc5': 1.461, 'Fc3': 2.07, 'Fc1': 1.014, 'Fcz': 5.036, 'Fc2': 5.072, 'Fc4': 4.133, 'Fc6': 5.154, 'C5': 0.256, 'C3': 1.15, 'C1': 0.634, 'Cz': 2.431, 'C2': 1.105, 'C4': 0.762, 'C6': 0.445, 'Cp5': 0.0, 'Cp3': 0.939, 'Cp1': 1.809, 'Cpz': 2.165, 'Cp2': 2.404, 'Cp4': 0.0, 'Cp6': 1.315, 'Fp1': 0.763, 'Fpz': 0.523, 'Fp2': 0.751, 'Af7': 0.169, 'Af3': 0.116, 'Afz': 0.577, 'Af4': 1.51, 'Af8': 0.121, 'F7': 0.85, 'F5': 0.368, 'F3': 0.718, 'F1': 0.686, 'Fz': 3.471, 'F2': 4.593, 'F4': 4.821, 'F6': 0.472, 'F8': 1.123, 'Ft7': 1.503, 'Ft8': 0.0, 'T7': 1.702, 'T8': 0.895, 'T9': 0.279, 'T10': 4.22, 'Tp7': 3.969, 'Tp8': 2.735, 'P7': 1.37, 'P5': 2.504, 'P3': 3.925, 'P1': 2.031, 'Pz': 3.767, 'P2': 0.231, 'P4': 3.575, 'P6': 1.963, 'P8': 2.699, 'Po7': 3.391, 'Po3': 3.863, 'Poz': 2.032, 'Po4': 3.586, 'Po8': 2.826, 'O1': 3.471, 'Oz': 2.862, 'O2': 2.297, 'Iz': 3.407}
Local degree:  {'Fc5': 1.461, 'Fc3': 2.07, 'Fc1': 1.014, 'Fcz': 5.036, 'Fc2': 5.072, 'Fc4': 4.133, 'Fc6': 5.154, 'C5': 0.256, 'C3': 1.15, 'C1': 0.634, 'Cz': 2.431, 'C2': 1.105, 'C4': 0.762, 'C6': 0.445, 'Cp5': 0.0, 'Cp3': 0.939, 'Cp1': 1.809, 'Cpz': 2.165, 'Cp2': 2.404, 'Cp4': 0.0, 'Cp6': 1.315, 'Fp1': 0.763, 'Fpz': 0.523, 'Fp2': 0.751, 'Af7': 0.169, 'Af3': 0.116, 'Afz': 0.577, 'Af4': 1.51, 'Af8': 0.121, 'F7': 0.85, 'F5': 0.368, 'F3': 0.718, 'F1': 0.686, 'Fz': 3.471, 'F2': 4.593, 'F4': 4.821, 'F6': 0.472, 'F8': 1.123, 'Ft7': 1.503, 'Ft8': 0.0, 'T7': 1.702, 'T8': 0.895, 'T9': 0.279, 'T10': 4.22, 'Tp7': 3.969, 'Tp8': 2.735, 'P7': 1.37, 'P5': 2.504, 'P3': 3.925, 'P1': 2.031, 'Pz': 3.767, 'P2': 0.231, 'P4': 3.575, 'P6': 1.963, 'P8': 2.699, 'Po7': 3.391, 'Po3': 3.863, 'Poz': 2.032, 'Po4': 3.586, 'Po8': 2.826, 'O1': 3.471, 'Oz': 2.862, 'O2': 2.297, 'Iz': 3.407}

print('\x1b[1;35m'+'Top 10'+'\x1b[0m \n')
local_indegree_dict = IDX.local_indegree(True)
local_outdegree_dict = IDX.local_outdegree(True)
local_degree_dict = IDX.local_degree(True)


print('\x1b[1;39m'+"Local indegree: "+'\x1b[0m\n')
print(npm.top10(local_indegree_dict))
print('\n\x1b[1;39m'+"Local outdegree: "+'\x1b[0m\n')
print(npm.top10(local_outdegree_dict))
print('\n\x1b[1;39m'+"Local degree: "+'\x1b[0m\n')
print(npm.top10(local_degree_dict))

Top 10 

Local indegree: 

{'Fc6': 5.154, 'Fc2': 5.072, 'Fcz': 5.036, 'F4': 4.821, 'F2': 4.593, 'T10': 4.22, 'Fc4': 4.133, 'Tp7': 3.969, 'P3': 3.925, 'Po3': 3.863}

Local outdegree: 

{'Af7': 4.653, 'F7': 4.605, 'F5': 4.518, 'Af3': 4.37, 'Fp1': 3.359, 'Fpz': 3.268, 'F3': 3.148, 'Oz': 2.829, 'Poz': 2.811, 'P8': 2.755}

Local degree: 

{'Po3': 6.369, 'O1': 6.197, 'F4': 6.178, 'Fc2': 6.132, 'Pz': 6.084, 'F2': 6.027, 'Fc6': 6.018, 'Fcz': 6.01, 'Po7': 5.868, 'Oz': 5.69}

3. Motif analysis

3.1 (mandatory)

Perform motifs analysis to investigate the presence of 3-node configurations in the networks: determine their frequency and statistical significance (motifs, anti-motifs).

## alpha waves
## threshold: 0.05
## method: PDC
## method for p: AIC

freq=10
threshold=0.05
method = 'PDC'

Open Eyes

# Create EO connectivity graph for 10Hz frequence
# then save the edges in txt file for mfinder app

EO = npm.Graph_for_motifs(method, 'EO', density_threshold=threshold, freq=freq, folder='data/')
EO.connectivity(freq=freq,threshold=threshold,plot=True)
EO.preprocess_for_mfinder()

loaded EO resting state data

best model order p: 7

created text file into 'data/EO_10Hz_0.05dt_Motifs.txt'

# performing motifs analysis with mfinder:

# mfinder ../data/EO_10Hz_0.05dt_Motifs.txt -s 3 -r 2000 -nu
# mfinder ../data/EC_10Hz_0.05dt_Motifs.txt -s 3 -r 2000 -nu

with open('data/EO_10Hz_0.05dt_Motifs_OUT.txt', 'r') as f:
    print (f.read())

   Summary motif results
   =====================
mfinder Version 1.20

MOTIF FINDER RESULTS:

	Network name: ../data/EO_10Hz_0.05dt_Motifs.txt
	Network type: Directed
	Num of Nodes: 63 Num of Edges: 201
	Num of Nodes with edges: 63
	Maximal out degree (out-hub) : 41
	Maximal in degree (in-hub) : 10
	Roots num: 7 Leaves num: 12
	Single Edges num: 181 Mutual Edges num: 7

	Motif size searched  3
	Total number of 3-node subgraphs : 1935
	Number of random networks generated : 2000
	Random networks generation method: Switches
	Num of Switches range: 100.0-200.0, Success switches Ratio:0.372+-0.01

The following motifs were found:

Criteria taken : Pval < 0.010
                 Mfactor > 1.10
                 Uniquness ignored

MOTIF	NREAL	NRAND		NREAL	NREAL	UNIQ	CREAL
ID		STATS		ZSCORE	PVAL	VAL	[MILI]	
108	17	7.5+-2.5	3.84	0.000	0	8.79

0 0 1 
1 0 1 
1 0 0 




Full list of subgraphs size 3 ids:

	( Total num of different subgraphs size 3 is : 13 )

MOTIF	NREAL	NRAND		NREAL	NREAL	CREAL	UNIQ
ID		STATS		ZSCORE	PVAL	[MILI]	
6	1366	1341.7+-6.5	3.74	0.000	705.94	0

12	155	149.6+-6.4	0.85	0.206	80.10	0

14	13	11.0+-1.7	1.22	0.288	6.72	0

36	265	255.5+-6.3	1.51	0.079	136.95	0

38	78	81.3+-6.2	-0.53	0.717	40.31	0

46	0	1.0+-0.8	-1.18	1.000	0.00	0

74	16	35.0+-5.0	-3.82	1.000	8.27	0

78	0	0.0+-0.0	888888	1.000	0.00	0

98	0	0.6+-0.8	-0.81	1.000	0.00	0

102	0	0.1+-0.3	-0.31	1.000	0.00	0

108	17	7.5+-2.5	3.84	0.000	8.79	0

110	0	0.0+-0.0	888888	1.000	0.00	0

238	4	4.0+-0.0	888888	1.000	2.07	0



 (Application total runtime was:   56.0 seconds.)

 (Real network processing runtime was:    0.0 seconds.)

 (Single Random network processing runtime was:    0.0 seconds.)

Summary for EO:

===============

2 motifs detected:

| ID | 6, 108 |

===============

1 anti-motifs detected:

| ID | x |

---

Closed Eyes

# Create EC connectivity graph for 10Hz frequence
# then save the edges in txt file for mfinder app

EC = npm.Graph_for_motifs(method, 'EC', density_threshold=threshold, freq=freq_alpha, folder='data/')
EC.connectivity(freq=freq,threshold=threshold,plot=True)
EC.preprocess_for_mfinder()

loaded EC resting state data

best model order p: 6

created text file into 'data/EC_10Hz_0.05dt_Motifs.txt'

with open('data/EC_10Hz_0.05dt_Motifs_OUT.txt', 'r') as f:
    print (f.read())

   Summary motif results
   =====================
mfinder Version 1.20

MOTIF FINDER RESULTS:

	Network name: ../data/EC_10Hz_0.05dt_Motifs.txt
	Network type: Directed
	Num of Nodes: 63 Num of Edges: 201
	Num of Nodes with edges: 63
	Maximal out degree (out-hub) : 9
	Maximal in degree (in-hub) : 14
	Roots num: 18 Leaves num: 1
	Single Edges num: 160 Mutual Edges num: 17

	Motif size searched  3
	Total number of 3-node subgraphs : 959
	Number of random networks generated : 2000
	Random networks generation method: Switches
	Num of Switches range: 100.0-200.0, Success switches Ratio:0.688+-0.01

The following motifs were found:

Criteria taken : Pval < 0.010
                 Mfactor > 1.10
                 Uniquness ignored

MOTIF	NREAL	NRAND		NREAL	NREAL	UNIQ	CREAL
ID		STATS		ZSCORE	PVAL	VAL	[MILI]	
38	59	25.9+-4.5	7.34	0.000	0	61.46

0 1 1 
0 0 1 
0 0 0 

46	7	0.8+-0.9	7.18	0.000	0	7.29

0 1 1 
1 0 1 
0 0 0 

108	32	9.8+-2.8	8.01	0.000	0	33.33

0 0 1 
1 0 1 
1 0 0 

238	3	0.4+-0.6	4.62	0.002	0	3.12

0 1 1 
1 0 1 
1 1 0 




Full list of subgraphs size 3 ids:

	( Total num of different subgraphs size 3 is : 13 )

MOTIF	NREAL	NRAND		NREAL	NREAL	CREAL	UNIQ
ID		STATS		ZSCORE	PVAL	[MILI]	
6	174	222.9+-5.1	-9.65	1.000	180.73	0

12	190	225.0+-5.8	-6.14	1.000	197.40	0

14	19	35.8+-2.6	-6.52	1.000	19.79	0

36	321	358.2+-4.9	-7.61	1.000	334.38	0

38	59	25.9+-4.5	7.34	0.000	61.46	0

46	7	0.8+-0.9	7.18	0.000	7.29	0

74	109	158.8+-5.8	-8.59	1.000	113.54	0

78	9	18.4+-2.1	-4.50	1.000	9.38	0

98	1	1.3+-1.2	-0.24	0.713	1.04	0

102	5	2.3+-1.5	1.84	0.070	5.21	0

108	32	9.8+-2.8	8.01	0.000	33.33	0

110	3	1.9+-1.3	0.86	0.281	3.12	0

238	3	0.4+-0.6	4.62	0.002	3.12	0



 (Application total runtime was:   1.40 minutes).

 (Real network processing runtime was:    0.0 seconds.)

 (Single Random network processing runtime was:    0.0 seconds.)

Summary for EC:

===============

4 motifs detected:

| ID | 38, 46, 108, 238|

===============

x anti-motifs detected

| ID | x |

---

3.2. (class ‘C’)

For the motif with pattern A → B ← C, create a topographical representation of the networks considering only the connections involved in this configuration.

Open Eyes

# Motif file structure: EO
with open('data/EO_10Hz_0.05dt_Motifs_MEMBERS.txt', 'r') as f:
    for _ in range(9):
        print(f.readline())
    
    print('\t...')

subgraph id = 36

Nreal : 265

=================================

Full list of 265 members:



1	24	2	

1	39	2	

1	36	33	

2	36	33	

	...

EO.plot_motifs_36('data/EO_10Hz_0.05dt_Motifs_MEMBERS.txt',
                  name='data/EO_0.05_motif36', save=False)

Closed Eyes

# Motif file structure: EC
with open('data/EC_10Hz_0.05dt_Motifs_MEMBERS.txt', 'r') as f:
    for _ in range(9):
        print(f.readline())
    
    print('\t...')

subgraph id = 36

Nreal : 321

=================================

Partial list of 320 members:



1	32	2	

11	53	52	

18	53	52	

19	45	20	

	...

EC.plot_motifs_36('data/EC_10Hz_0.05dt_Motifs_MEMBERS.txt',
                   name='data/EC_0.05_motif36', save=False)

3.3. (class ‘C’)

Choose a channel selected in parieto-occipital scalp region and determine the motifs which involve it.

Open Eyes

df_prova = EO.find_motifs(channel='Po3')
df_prova

	motif	occurrencies
0	A->B<-C	20
4	A->B<-C, A->C	1
1	A->B->C	0
2	A<->B<-C	0
3	A<-B->C	0
5	A<-B->C, A<->C	0
6	A<->B->C	0
7	A<->B<->C	0
8	A<-B<-C, A->C	0
9	A->B->C, A<->C	0
10	A->B<-C, A<->C	0
11	A->B<->C, A<->C	0
12	A<->B<->C, A<->C	0

Closed Eyes

EC.find_motifs('Po3')

	motif	occurrencies
0	A->B<-C	32
2	A<->B<-C	18
1	A->B->C	17
4	A->B<-C, A->C	11
6	A<->B->C	4
5	A<-B->C, A<->C	3
9	A->B->C, A<->C	3
10	A->B<-C, A<->C	2
3	A<-B->C	1
7	A<->B<->C	1
8	A<-B<-C, A->C	1
11	A->B<->C, A<->C	1
12	A<->B<->C, A<->C	0

Additional: let's see the motifs in configuration A-->B<--C involving Po3 channel

# channel selected: Po3 (57)
# in our notation (from 0 to 64) Po3 correspond to 56

# we can directly plot it with our figure
# remaking member analysis

# plot for EO (pattern A->B<-C)
EO.plot_motifs_36('data/EO_10Hz_0.05dt_Motifs_MEMBERS.txt', 'EO_motifsPlot_Po3', 
                  single_channel=56, motifcolor="red", save=False, tipsize=0.5)

# plot for EC (pattern A->B<-C)
EC.plot_motifs_36('data/EC_10Hz_0.05dt_Motifs_MEMBERS.txt', 'EC_motifsPlot_Po3', 
                  single_channel=56, motifcolor="red", save=False, tipsize=0.5)

4. (class ‘E’)

Perform the same analysis described in task 3.1 considering 4-node motifs

For motifs detection we used the following rules:

• motifs: Zscore > 3, pvalue < 0.05
• antimotifs: Zscore < -3, pvalue > 0.95

For motifs_ID's you can check the legend https://www.weizmann.ac.il/mcb/UriAlon/sites/mcb.UriAlon/files/uploads/NetworkMotifsSW/mfinder/motifdictionary.pdf

Open Eyes

with open('data/EO_10Hz_0.05dt_4_Motifs_OUT.txt', 'r') as f:
    for _ in range(10):
        print(f.readline(), end='')
        
print('...\n...\nCheck EO_10Hz_0.05dt_4_Motifs_OUT.txt for more info')

MOTIF	NREAL	NRAND		NREAL	NREAL	CREAL	UNIQ
ID		STATS		ZSCORE	PVAL	[MILI]	
14	12464	11921.6+-178.8	3.03	0.002	556.97	0

28	53	81.5+-11.5	-2.48	0.996	2.37	0

30	34	29.6+-7.5	0.59	0.297	1.52	0

74	1696	1200.1+-138.0	3.59	0.000	75.79	0

...
...
Check EO_10Hz_0.05dt_4_Motifs_OUT.txt for more info

npm.detect_4motifs(resting_state='EO')

Saving motifs in  data/EO_4Motif.csv ...done
Saving antimotifs in  data/EO_4Antimotif.csv ...done

pd.read_csv('data/EO_4Motif.csv').head(20)

	Motif_ID	N_real	Nrand	Zscore	pvalue
0	4678	38	0.6+-1.4	27.73	0.000
1	4686	6	0.2+-0.5	11.50	0.001
2	5016	6	0.3+-0.7	8.13	0.002
3	6552	17	2.6+-2.1	6.72	0.000
4	904	56	18.2+-7.2	5.25	0.000
5	74	1696	1200.1+-138.0	3.59	0.000
6	6616	2	0.2+-0.5	3.27	0.030
7	15258	9	3.1+-1.9	3.04	0.003
8	14	12464	11921.6+-178.8	3.03	0.002

pd.read_csv('data/EO_4Antimotif.csv').head(20)

	Motif_ID	N_real	Nrand	Zscore	pvalue
0	4950	7	27.1+-5.2	-3.86	1.000
1	328	65	126.8+-22.8	-2.71	1.000
2	4370	11	35.9+-9.7	-2.57	1.000
3	2186	142	172.2+-11.9	-2.53	0.993
4	472	28	54.2+-10.3	-2.53	0.996

Closed Eyes

with open('data/EC_10Hz_0.05dt_4_Motifs_OUT.txt', 'r') as f:
    for _ in range(10):
        print(f.readline(), end='')
        
print('...\n...\nCheck EC_10Hz_0.05dt_4_Motifs_OUT.txt for more info')

MOTIF	NREAL	NRAND		NREAL	NREAL	CREAL	UNIQ
ID		STATS		ZSCORE	PVAL	[MILI]	
14	138	203.2+-16.3	-4.02	1.000	25.59	0

28	65	190.9+-15.8	-7.93	1.000	12.15	0

30	4	27.4+-4.9	-4.72	1.000	0.74	0

74	381	528.4+-39.1	-3.77	1.000	70.77	0

...
...
Check EC_10Hz_0.05dt_4_Motifs_OUT.txt for more info

npm.detect_4motifs(resting_state='EC')

Saving motifs in  data/EC_4Motif.csv ...done
Saving antimotifs in  data/EC_4Antimotif.csv ...done

pd.read_csv('data/EC_4Motif.csv').head(20)

	Motif_ID	N_real	Nrand	Zscore	pvalue
0	6358	1	0.0+-0.0	44.700001	0.001
1	2270	2	0.0+-0.1	35.720001	0.000
2	15258	6	0.0+-0.2	32.240002	0.000
3	6614	1	0.0+-0.0	25.790001	0.002
4	222	5	0.0+-0.2	24.139999	0.000
5	15262	1	0.0+-0.1	18.230000	0.003
6	2510	1	0.0+-0.1	13.260000	0.005
7	6552	36	3.1+-2.6	12.590000	0.000
8	6616	6	0.2+-0.5	12.560000	0.000
9	906	17	1.8+-1.5	10.140000	0.000
10	13278	1	0.0+-0.1	9.140000	0.011
11	6364	3	0.1+-0.3	8.400000	0.001
12	5020	4	0.2+-0.5	8.010000	0.001
13	2254	5	0.3+-0.6	7.920000	0.001
14	206	17	2.8+-2.0	7.260000	0.000
15	13142	10	0.6+-1.3	7.110000	0.000
16	6602	1	0.0+-0.1	7.060000	0.019
17	394	68	21.9+-6.9	6.730000	0.000
18	4678	37	8.5+-4.5	6.280000	0.000
19	4572	9	1.3+-1.3	5.690000	0.000

pd.read_csv('data/EC_4Antimotif.csv').head(20)

	Motif_ID	N_real	Nrand	Zscore	pvalue
0	76	1079	1687.8+-74.9	-8.13	1.000
1	28	65	190.9+-15.8	-7.93	1.000
2	4370	213	384.6+-29.5	-5.81	1.000
3	2184	441	583.0+-26.0	-5.47	1.000
4	4428	41	150.5+-20.2	-5.43	1.000
5	282	36	94.4+-11.0	-5.32	1.000
6	404	215	358.2+-27.7	-5.16	1.000
7	396	32	130.3+-19.3	-5.08	1.000
8	4420	153	324.9+-34.4	-5.00	1.000
9	4374	20	75.2+-11.4	-4.82	1.000
10	392	565	878.5+-65.6	-4.77	1.000
11	30	4	27.4+-4.9	-4.72	1.000
12	406	3	33.8+-7.6	-4.07	1.000
13	14	138	203.2+-16.3	-4.02	1.000
14	332	55	148.5+-23.6	-3.95	1.000
15	74	381	528.4+-39.1	-3.77	1.000
16	328	153	268.9+-34.1	-3.40	1.000
17	286	2	11.8+-3.0	-3.25	1.000
18	390	10	40.8+-10.2	-3.01	1.000
19	280	245	310.1+-22.1	-2.95	0.999

4. Community detection

4.1. (mandatory)

Determine number and composition (i.e. list of nodes) of the communities obtained applying one of the algorithms introduced during the course.

Louvain_Algorithm - Optimal_Modularity

Open Eyes

PDC_open = npm.Connectivity_Graph('PDC')
PDC_open.import_data(open_eyes)
PDC_open.connectivity(freq=freq_alpha,threshold=0.2,plot=True)

best model order p: 7

Closed Eyes

PDC_closed = npm.Connectivity_Graph('PDC')
PDC_closed.import_data(closed_eyes)
PDC_closed.connectivity(freq=freq_alpha,threshold=0.2,plot=True)

best model order p: 6

4.2. (class ‘B’)

Make a graphical representation of the community structure in both rest conditions.

Open Eyes

comms_mod_open,graph = PDC_open.optimal_modularity_community_detection(name='optimal_modularity_open_eyes')
graph

The estimated number of communities is 5


Communities
Community number 0 - size: 21
Community number 1 - size: 13
Community number 2 - size: 12
Community number 3 - size: 10
Community number 4 - size: 8

The communities are:

odict_items([(0, ['C3', 'C1', 'Cz', 'C2', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'Po3', 'Poz', 'Po4', 'O1', 'O2']), (1, ['Fp1', 'Fpz', 'Fp2', 'Afz', 'Af4', 'Af8', 'F8', 'T8', 'T9', 'T10', 'Tp8', 'P8', 'Iz']), (2, ['Fc5', 'Fc3', 'Fc1', 'Af7', 'F7', 'F5', 'F3', 'Fz', 'F6', 'Ft7', 'T7', 'P5']), (3, ['C5', 'C4', 'C6', 'Cp5', 'Ft8', 'Tp7', 'P7', 'Po7', 'Po8', 'Oz']), (4, ['Fcz', 'Fc2', 'Fc4', 'Fc6', 'Af3', 'F1', 'F2', 'F4'])])

Closed Eyes

comms_mod_closed,graph = PDC_closed.optimal_modularity_community_detection(name='optimal_modularity_closed_eyes')
graph

The estimated number of communities is 4


Communities
Community number 0 - size: 22
Community number 1 - size: 18
Community number 2 - size: 15
Community number 3 - size: 9

The communities are:

odict_items([(0, ['C5', 'C3', 'Cz', 'Cp5', 'Cp3', 'Cp1', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P8', 'Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'Oz', 'O2', 'Iz']), (1, ['Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'C1', 'C2', 'Fpz', 'Fp2', 'Afz', 'Af4', 'Af8', 'Fz', 'F2', 'F4', 'F6', 'F8', 'Ft8']), (2, ['Fc5', 'Fc3', 'Cpz', 'Cp2', 'Fp1', 'Af7', 'Af3', 'F7', 'F5', 'F3', 'F1', 'Ft7', 'T7', 'T9', 'Tp7']), (3, ['C4', 'C6', 'Cp4', 'Cp6', 'T8', 'T10', 'Tp8', 'P4', 'P6'])])

4.3. (class ‘C’)

Compare the community structure obtained by means of two different methods (modularity-based vs information theory-based approaches).

Infomap_Algorithm_random_walk - Information_Theory

Open Eyes

comms_info_open,graph  = PDC_open.infomap_community_detection(name='information_theory_open_eyes')
graph

The estimated number of communities is 4


Communities
Community number 0 - size: 25
Community number 1 - size: 33
Community number 2 - size: 2
Community number 3 - size: 4

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'Cp5', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'Fp1', 'Fpz', 'Fp2', 'Af7']), (1, ['Af3', 'Afz', 'Af4', 'Af8', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'Ft7', 'Ft8', 'T7', 'T8', 'T9', 'T10', 'Tp7', 'Tp8', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'P8', 'Po7', 'Po3', 'Poz']), (2, ['Po4', 'Po8']), (3, ['O1', 'Oz', 'O2', 'Iz'])])

Communities comparison for open_eyes_case

M = np.zeros([len(comms_mod_open),len(comms_info_open)])
for i in range(len(comms_mod_open)):
    for j in range(len(comms_info_open)):
        #JACCARD SIMILARITY
        M[i,j] = len(set(comms_mod_open[i]).intersection(set(comms_info_open[j])))/len(set(comms_mod_open[i]).union(set(comms_info_open[j])))

M = pd.DataFrame(M)
plt.figure(figsize=(9,9))
ax = sns.heatmap(M,xticklabels=M.columns+1,yticklabels=M.columns+1,annot=True,cmap='viridis',linewidths=1,square=True)
ax.invert_yaxis()
plt.xlabel('Infomap',fontsize=12)
xlabels = comms_info_open.keys()
ylabels = comms_mod_open.keys()
plt.xticks(np.arange(len(comms_info_open))+0.5, xlabels,)
plt.yticks(np.arange(len(comms_mod_open))+0.5, ylabels)
plt.ylim(0,len(comms_mod_open))
plt.ylabel('Optimal_Modularity_Method',fontsize=12)
plt.title('Jaccard Similarity',fontsize=15)
plt.tight_layout()
plt.show()

comms_info_closed,graph = PDC_closed.infomap_community_detection(name='information_theory_closed_eyes')
graph

The estimated number of communities is 5


Communities
Community number 0 - size: 5
Community number 1 - size: 7
Community number 2 - size: 39
Community number 3 - size: 12
Community number 4 - size: 1

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2']), (1, ['Fc4', 'Fc6', 'C5', 'C3', 'C1', 'Cz', 'C2']), (2, ['C4', 'C6', 'Cp5', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'Fp1', 'Fpz', 'Fp2', 'Af7', 'Af3', 'Afz', 'Af4', 'Af8', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'Ft7', 'Ft8', 'T7', 'T8', 'T9', 'T10', 'Tp7', 'Tp8', 'P7', 'P5', 'P3', 'P1', 'Pz']), (3, ['P2', 'P4', 'P6', 'P8', 'Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'Oz', 'O2']), (4, ['Iz'])])

Communities comparison for closed_eyes_case

M = np.zeros([len(comms_mod_closed),len(comms_info_closed)])
for i in range(len(comms_mod_closed)):
    for j in range(len(comms_info_closed)):
        #JACCARD SIMILARITY
        M[i,j] = len(set(comms_mod_closed[i]).intersection(set(comms_info_closed[j])))/len(set(comms_mod_closed[i]).union(set(comms_info_closed[j])))

M = pd.DataFrame(M)

plt.figure(figsize=(9,9))
ax = sns.heatmap(M,xticklabels=M.columns+1,yticklabels=M.columns+1,annot=True,cmap='viridis',linewidths=1,square=True)
ax.invert_yaxis()
plt.xlabel('Infomap',fontsize=12)
xlabels = comms_info_closed.keys()
ylabels = comms_mod_closed.keys()
plt.xticks(np.arange(len(comms_info_closed))+0.5, xlabels,)
plt.yticks(np.arange(len(comms_mod_closed))+0.5, ylabels)
plt.ylim(0,len(comms_mod_closed))
plt.ylabel('Optimal_Modularity_Method',fontsize=12)
plt.title('Jaccard Similarity',fontsize=15)
plt.tight_layout()
plt.show()

plot of how number of communities varies for both resting cases for both methods

##densities threshold

densities = [0.05,0.1,0.2,0.3,0.5]
m1 = []
m2 = []
m3 = []
m4 = []

for d in tqdm_notebook(densities):
    PDC_open = npm.Connectivity_Graph('PDC')
    PDC_open.import_data(open_eyes)
    PDC_open.connectivity(freq=freq_alpha,threshold=d,plot=False,order=order_open)
    PDC_closed = npm.Connectivity_Graph('PDC')
    PDC_closed.import_data(closed_eyes)
    PDC_closed.connectivity(freq=freq_alpha,threshold=d,plot=False,order=order_closed)
    comms_open_mod = PDC_open.optimal_modularity_community_detection(visual = False)
    comms_closed_mod = PDC_closed.optimal_modularity_community_detection(visual = False)
    comms_open_info = PDC_open.infomap_community_detection(visual = False)
    comms_closed_info = PDC_closed.infomap_community_detection(visual = False)
    m1.append(len(comms_open_mod))
    m2.append(len(comms_open_info))
    m3.append(len(comms_closed_mod))
    m4.append(len(comms_closed_info))

The estimated number of communities is 5


Communities
Community number 0 - size: 21
Community number 1 - size: 19
Community number 2 - size: 14
Community number 3 - size: 5
Community number 4 - size: 5

The communities are:

odict_items([(0, ['C5', 'Cz', 'C2', 'C4', 'C6', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Ft8', 'Tp7', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P8', 'Po4', 'Po8', 'Oz']), (1, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'C1', 'Cp5', 'Cp6', 'Fp2', 'Af7', 'Af3', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F6', 'Ft7', 'P5']), (2, ['C3', 'Fp1', 'Fpz', 'Af4', 'Af8', 'F8', 'T8', 'T9', 'T10', 'Tp8', 'P6', 'Poz', 'O1', 'Iz']), (3, ['T7', 'P7', 'Po7', 'Po3', 'O2']), (4, ['Fc4', 'Fc6', 'Afz', 'F2', 'F4'])])
The estimated number of communities is 6


Communities
Community number 0 - size: 17
Community number 1 - size: 14
Community number 2 - size: 11
Community number 3 - size: 10
Community number 4 - size: 6
Community number 5 - size: 6

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'Fp1', 'Af7', 'Af3', 'F7', 'F5', 'F3', 'F1', 'Fz', 'Ft7', 'T7', 'T9', 'Tp7']), (1, ['Cp1', 'P7', 'P1', 'P2', 'P8', 'Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'Oz', 'O2', 'Iz']), (2, ['Fc4', 'Fc6', 'Fpz', 'Fp2', 'Afz', 'Af4', 'Af8', 'F2', 'F4', 'F6', 'F8']), (3, ['C4', 'C6', 'Cp4', 'Cp6', 'Ft8', 'T8', 'T10', 'Tp8', 'P4', 'P6']), (4, ['C1', 'Cz', 'C2', 'Cpz', 'Cp2', 'Pz']), (5, ['C5', 'C3', 'Cp5', 'Cp3', 'P5', 'P3'])])
The estimated number of communities is 8


Communities
Community number 0 - size: 28
Community number 1 - size: 2
Community number 2 - size: 4
Community number 3 - size: 8
Community number 4 - size: 11
Community number 5 - size: 2
Community number 6 - size: 7
Community number 7 - size: 2

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'Cp5', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'Fp1', 'Fpz', 'Fp2', 'Af7', 'Af3', 'Afz', 'Af4']), (1, ['Af8', 'F7']), (2, ['F5', 'F3', 'F1', 'Fz']), (3, ['F2', 'F4', 'F6', 'F8', 'Ft7', 'Ft8', 'T7', 'T8']), (4, ['T9', 'T10', 'Tp7', 'Tp8', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4']), (5, ['P6', 'P8']), (6, ['Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'Oz']), (7, ['O2', 'Iz'])])
The estimated number of communities is 6


Communities
Community number 0 - size: 15
Community number 1 - size: 4
Community number 2 - size: 10
Community number 3 - size: 11
Community number 4 - size: 11
Community number 5 - size: 13

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'Cp5']), (1, ['Cp3', 'Cp1', 'Cpz', 'Cp2']), (2, ['Cp4', 'Cp6', 'Fp1', 'Fpz', 'Fp2', 'Af7', 'Af3', 'Afz', 'Af4', 'Af8']), (3, ['F7', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'Ft7', 'Ft8']), (4, ['T7', 'T8', 'T9', 'T10', 'Tp7', 'Tp8', 'P7', 'P5', 'P3', 'P1', 'Pz']), (5, ['P2', 'P4', 'P6', 'P8', 'Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'Oz', 'O2', 'Iz'])])
The estimated number of communities is 5


Communities
Community number 0 - size: 19
Community number 1 - size: 14
Community number 2 - size: 14
Community number 3 - size: 12
Community number 4 - size: 5

The communities are:

odict_items([(0, ['C5', 'Cz', 'C2', 'C4', 'C6', 'Cp5', 'Cp2', 'Cp4', 'Cp6', 'F6', 'Ft8', 'T9', 'Tp7', 'P7', 'P4', 'P6', 'Po8', 'Oz', 'O2']), (1, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'Af7', 'Af3', 'F7', 'F5', 'F3', 'Fz', 'Ft7', 'T7', 'P5']), (2, ['C3', 'C1', 'Cp3', 'Cp1', 'Cpz', 'P3', 'P1', 'Pz', 'P2', 'Po7', 'Po3', 'Poz', 'Po4', 'O1']), (3, ['Fp1', 'Fpz', 'Fp2', 'Afz', 'Af4', 'Af8', 'F8', 'T8', 'T10', 'Tp8', 'P8', 'Iz']), (4, ['Fc4', 'Fc6', 'F1', 'F2', 'F4'])])
The estimated number of communities is 5


Communities
Community number 0 - size: 15
Community number 1 - size: 14
Community number 2 - size: 13
Community number 3 - size: 12
Community number 4 - size: 10

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Cpz', 'Fp1', 'Af7', 'Af3', 'F7', 'F5', 'F3', 'F1', 'Ft7', 'T7', 'T9', 'Tp7']), (1, ['Fcz', 'Fc2', 'Fc4', 'Fc6', 'Fpz', 'Fp2', 'Afz', 'Af4', 'Af8', 'Fz', 'F2', 'F4', 'F6', 'F8']), (2, ['C5', 'C3', 'C1', 'Cz', 'Cp5', 'Cp3', 'Cp1', 'P7', 'P5', 'P3', 'P1', 'Pz', 'Po3']), (3, ['C2', 'C4', 'C6', 'Cp2', 'Cp4', 'Cp6', 'Ft8', 'T8', 'T10', 'Tp8', 'P4', 'P6']), (4, ['P2', 'P8', 'Po7', 'Poz', 'Po4', 'Po8', 'O1', 'Oz', 'O2', 'Iz'])])
The estimated number of communities is 4


Communities
Community number 0 - size: 34
Community number 1 - size: 4
Community number 2 - size: 17
Community number 3 - size: 9

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'Cp5', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'Fp1', 'Fpz', 'Fp2', 'Af7', 'Af3', 'Afz', 'Af4', 'Af8', 'F7', 'F5', 'F3', 'F1', 'Fz']), (1, ['F2', 'F4', 'F6', 'F8']), (2, ['Ft7', 'Ft8', 'T7', 'T8', 'T9', 'T10', 'Tp7', 'Tp8', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'P8']), (3, ['Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'Oz', 'O2', 'Iz'])])
The estimated number of communities is 7


Communities
Community number 0 - size: 20
Community number 1 - size: 13
Community number 2 - size: 25
Community number 3 - size: 1
Community number 4 - size: 1
Community number 5 - size: 3
Community number 6 - size: 1

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'Cp5', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4']), (1, ['Cp6', 'Fp1', 'Fpz', 'Fp2', 'Af7', 'Af3', 'Afz', 'Af4', 'Af8', 'F7', 'F5', 'F3', 'F1']), (2, ['Fz', 'F2', 'F4', 'F6', 'F8', 'Ft7', 'Ft8', 'T7', 'T8', 'T9', 'T10', 'Tp7', 'Tp8', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'P8', 'Po7', 'Po3', 'Poz']), (3, ['Po4']), (4, ['Po8']), (5, ['O1', 'Oz', 'O2']), (6, ['Iz'])])
The estimated number of communities is 5


Communities
Community number 0 - size: 21
Community number 1 - size: 13
Community number 2 - size: 12
Community number 3 - size: 10
Community number 4 - size: 8

The communities are:

odict_items([(0, ['C3', 'C1', 'Cz', 'C2', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'Po3', 'Poz', 'Po4', 'O1', 'O2']), (1, ['Fp1', 'Fpz', 'Fp2', 'Afz', 'Af4', 'Af8', 'F8', 'T8', 'T9', 'T10', 'Tp8', 'P8', 'Iz']), (2, ['Fc5', 'Fc3', 'Fc1', 'Af7', 'F7', 'F5', 'F3', 'Fz', 'F6', 'Ft7', 'T7', 'P5']), (3, ['C5', 'C4', 'C6', 'Cp5', 'Ft8', 'Tp7', 'P7', 'Po7', 'Po8', 'Oz']), (4, ['Fcz', 'Fc2', 'Fc4', 'Fc6', 'Af3', 'F1', 'F2', 'F4'])])
The estimated number of communities is 4


Communities
Community number 0 - size: 22
Community number 1 - size: 18
Community number 2 - size: 15
Community number 3 - size: 9

The communities are:

odict_items([(0, ['C5', 'C3', 'Cz', 'Cp5', 'Cp3', 'Cp1', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P8', 'Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'Oz', 'O2', 'Iz']), (1, ['Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'C1', 'C2', 'Fpz', 'Fp2', 'Afz', 'Af4', 'Af8', 'Fz', 'F2', 'F4', 'F6', 'F8', 'Ft8']), (2, ['Fc5', 'Fc3', 'Cpz', 'Cp2', 'Fp1', 'Af7', 'Af3', 'F7', 'F5', 'F3', 'F1', 'Ft7', 'T7', 'T9', 'Tp7']), (3, ['C4', 'C6', 'Cp4', 'Cp6', 'T8', 'T10', 'Tp8', 'P4', 'P6'])])
The estimated number of communities is 4


Communities
Community number 0 - size: 25
Community number 1 - size: 33
Community number 2 - size: 2
Community number 3 - size: 4

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'Cp5', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'Fp1', 'Fpz', 'Fp2', 'Af7']), (1, ['Af3', 'Afz', 'Af4', 'Af8', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'Ft7', 'Ft8', 'T7', 'T8', 'T9', 'T10', 'Tp7', 'Tp8', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'P8', 'Po7', 'Po3', 'Poz']), (2, ['Po4', 'Po8']), (3, ['O1', 'Oz', 'O2', 'Iz'])])
The estimated number of communities is 5


Communities
Community number 0 - size: 5
Community number 1 - size: 7
Community number 2 - size: 39
Community number 3 - size: 12
Community number 4 - size: 1

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2']), (1, ['Fc4', 'Fc6', 'C5', 'C3', 'C1', 'Cz', 'C2']), (2, ['C4', 'C6', 'Cp5', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'Fp1', 'Fpz', 'Fp2', 'Af7', 'Af3', 'Afz', 'Af4', 'Af8', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'Ft7', 'Ft8', 'T7', 'T8', 'T9', 'T10', 'Tp7', 'Tp8', 'P7', 'P5', 'P3', 'P1', 'Pz']), (3, ['P2', 'P4', 'P6', 'P8', 'Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'Oz', 'O2']), (4, ['Iz'])])
The estimated number of communities is 4


Communities
Community number 0 - size: 22
Community number 1 - size: 22
Community number 2 - size: 14
Community number 3 - size: 6

The communities are:

odict_items([(0, ['C5', 'Cp5', 'Fp1', 'Fpz', 'Fp2', 'Af7', 'Afz', 'Af4', 'Af8', 'F6', 'F8', 'Ft8', 'T8', 'T9', 'T10', 'Tp7', 'Tp8', 'P7', 'P8', 'Po8', 'Oz', 'Iz']), (1, ['C3', 'Cz', 'C2', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'Po7', 'Po3', 'Poz', 'Po4', 'O1', 'O2']), (2, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'C1', 'Af3', 'F7', 'F5', 'F3', 'F1', 'Fz', 'Ft7', 'T7']), (3, ['Fc4', 'Fc6', 'C4', 'C6', 'F2', 'F4'])])
The estimated number of communities is 4


Communities
Community number 0 - size: 17
Community number 1 - size: 16
Community number 2 - size: 16
Community number 3 - size: 15

The communities are:

odict_items([(0, ['Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'Fp1', 'Fpz', 'Fp2', 'Afz', 'Af4', 'Af8', 'Fz', 'F2', 'F4', 'F6', 'F8', 'Ft8']), (1, ['Fc5', 'Fc3', 'C5', 'C3', 'Cp5', 'Cp3', 'Af7', 'Af3', 'F7', 'F5', 'F3', 'F1', 'Ft7', 'T7', 'Tp7', 'P5']), (2, ['C1', 'Cz', 'C2', 'C4', 'C6', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'T8', 'Tp8', 'P1', 'Pz', 'P4', 'P6']), (3, ['T9', 'T10', 'P7', 'P3', 'P2', 'P8', 'Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'Oz', 'O2', 'Iz'])])
The estimated number of communities is 3


Communities
Community number 0 - size: 24
Community number 1 - size: 36
Community number 2 - size: 4

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'Cp5', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'Fp1', 'Fpz', 'Fp2']), (1, ['Af7', 'Af3', 'Afz', 'Af4', 'Af8', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'Ft7', 'Ft8', 'T7', 'T8', 'T9', 'T10', 'Tp7', 'Tp8', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'P8', 'Po7', 'Po3', 'Poz', 'Po4', 'Po8']), (2, ['O1', 'Oz', 'O2', 'Iz'])])
The estimated number of communities is 1


Communities
Community number 0 - size: 64

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'Cp5', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'Fp1', 'Fpz', 'Fp2', 'Af7', 'Af3', 'Afz', 'Af4', 'Af8', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'Ft7', 'Ft8', 'T7', 'T8', 'T9', 'T10', 'Tp7', 'Tp8', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'P8', 'Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'Oz', 'O2', 'Iz'])])
The estimated number of communities is 4


Communities
Community number 0 - size: 26
Community number 1 - size: 25
Community number 2 - size: 7
Community number 3 - size: 6

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'C1', 'Fp1', 'Fpz', 'Fp2', 'Af7', 'Af3', 'Afz', 'Af4', 'Af8', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F6', 'F8', 'Ft7', 'T7', 'T9', 'Tp8', 'Iz']), (1, ['C3', 'C2', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'T8', 'T10', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'P8', 'Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'O2']), (2, ['C5', 'Cz', 'Cp5', 'Ft8', 'Tp7', 'P7', 'Oz']), (3, ['Fc4', 'Fc6', 'C4', 'C6', 'F2', 'F4'])])
The estimated number of communities is 4


Communities
Community number 0 - size: 20
Community number 1 - size: 17
Community number 2 - size: 15
Community number 3 - size: 12

The communities are:

odict_items([(0, ['C5', 'C3', 'Cz', 'Cp5', 'Cp3', 'T9', 'P7', 'P5', 'P3', 'P2', 'P8', 'Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'Oz', 'O2', 'Iz']), (1, ['Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'Cpz', 'Fpz', 'Fp2', 'Afz', 'Af4', 'Af8', 'Fz', 'F2', 'F4', 'F6', 'F8', 'Ft8']), (2, ['C1', 'C2', 'C4', 'C6', 'Cp1', 'Cp2', 'Cp4', 'Cp6', 'T8', 'T10', 'Tp8', 'P1', 'Pz', 'P4', 'P6']), (3, ['Fc5', 'Fc3', 'Fp1', 'Af7', 'Af3', 'F7', 'F5', 'F3', 'F1', 'Ft7', 'T7', 'Tp7'])])
The estimated number of communities is 1


Communities
Community number 0 - size: 64

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'Cp5', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'Fp1', 'Fpz', 'Fp2', 'Af7', 'Af3', 'Afz', 'Af4', 'Af8', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'Ft7', 'Ft8', 'T7', 'T8', 'T9', 'T10', 'Tp7', 'Tp8', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'P8', 'Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'Oz', 'O2', 'Iz'])])
The estimated number of communities is 1


Communities
Community number 0 - size: 64

The communities are:

odict_items([(0, ['Fc5', 'Fc3', 'Fc1', 'Fcz', 'Fc2', 'Fc4', 'Fc6', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'Cp5', 'Cp3', 'Cp1', 'Cpz', 'Cp2', 'Cp4', 'Cp6', 'Fp1', 'Fpz', 'Fp2', 'Af7', 'Af3', 'Afz', 'Af4', 'Af8', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'Ft7', 'Ft8', 'T7', 'T8', 'T9', 'T10', 'Tp7', 'Tp8', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'P8', 'Po7', 'Po3', 'Poz', 'Po4', 'Po8', 'O1', 'Oz', 'O2', 'Iz'])])

Open Eyes

# set width of bar
barWidth = 0.25
 
# set height of bar
bars1 = m1
bars2 = m2

# Set position of bar on X axis
r1 = np.arange(len(bars1))
r2 = [x + barWidth for x in r1]

 
# Make the plot
plt.bar(r1, bars1, color='#7f6d5f', width=barWidth, edgecolor='white', label='Louvain')
plt.bar(r2, bars2, color='#557f2d', width=barWidth, edgecolor='white', label='Infomap')
 
# Add xticks on the middle of the group bars
plt.xlabel('graph density', fontweight='bold')
plt.xticks([r + barWidth for r in range(len(bars1))], densities)
 
# Create legend & Show graphic
plt.title('Number of communities - Open Eyes Case')
plt.legend()
plt.show()

Closed Eyes

# set width of bar
barWidth = 0.25
 
# set height of bar
bars1 = m3
bars2 = m4

# Set position of bar on X axis
r1 = np.arange(len(bars1))
r2 = [x + barWidth for x in r1]

 
# Make the plot
plt.bar(r1, bars1, color='#7f6d5f', width=barWidth, edgecolor='white', label='Louvain')
plt.bar(r2, bars2, color='#557f2d', width=barWidth, edgecolor='white', label='Infomap')
 
# Add xticks on the middle of the group bars
plt.xlabel('graph density', fontweight='bold')
plt.xticks([r + barWidth for r in range(len(bars1))], densities)
 
# Create legend & Show graphic
plt.title('Number of communities - Closed Eyes Case')
plt.legend()
plt.show()